docs/notebooks/JuliaCon2019.jl.ipynb

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   "source": [
    "# TSML (Time Series Machine Learning)\n",
    "- **Speaker:  Paulito Palmes**\n",
    "- **IBM Dublin Research Lab**\n",
    "- July 23, 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Motivations\n",
    "- innovations in industry sectors brought automations \n",
    "- automations require installation of sensor networks \n",
    "- main challenges:\n",
    "  - collect large volume of data, detect anomalies, monitor status\n",
    "  - discover patterns to reduce downtimes and manufacturing errors\n",
    "  - reduce energy usage\n",
    "  - predict faults/failures\n",
    "  - effective maintenance schedules\n",
    "\n",
    "_TSML leverages AI and ML libraries from ScikitLearn, Caret, and Julia as building blocks for processing huge amount of industrial time series data._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Typical TSML Workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## First, let's create an artificial data with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling TSML [198dc43e-9e51-5cd7-9d40-d9794d335912]\n"
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    },
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       "\\begin{tabular}{r|cc}\n",
       "\t& Date & Value\\\\\n",
       "\t\\hline\n",
       "\t& DateTime & Float64?\\\\\n",
       "\t\\hline\n",
       "\t1 & 2014-01-01T00:00:00 & 0.9063 \\\\\n",
       "\t2 & 2014-01-01T00:15:00 & 0.443494 \\\\\n",
       "\t3 & 2014-01-01T00:30:00 & 0.745673 \\\\\n",
       "\t4 & 2014-01-01T00:45:00 & 0.512083 \\\\\n",
       "\t5 & 2014-01-01T01:00:00 & \\emph{missing} \\\\\n",
       "\t6 & 2014-01-01T01:15:00 & 0.334152 \\\\\n",
       "\t7 & 2014-01-01T01:30:00 & \\emph{missing} \\\\\n",
       "\t8 & 2014-01-01T01:45:00 & \\emph{missing} \\\\\n",
       "\t9 & 2014-01-01T02:00:00 & 0.0991336 \\\\\n",
       "\t10 & 2014-01-01T02:15:00 & 0.125287 \\\\\n",
       "\\end{tabular}\n"
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       "\u001b[1m10×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m Date                \u001b[0m\u001b[1m Value           \u001b[0m\n",
       "     │\u001b[90m DateTime            \u001b[0m\u001b[90m Float64?        \u001b[0m\n",
       "─────┼──────────────────────────────────────\n",
       "   1 │ 2014-01-01T00:00:00        0.9063\n",
       "   2 │ 2014-01-01T00:15:00        0.443494\n",
       "   3 │ 2014-01-01T00:30:00        0.745673\n",
       "   4 │ 2014-01-01T00:45:00        0.512083\n",
       "   5 │ 2014-01-01T01:00:00 \u001b[90m missing         \u001b[0m\n",
       "   6 │ 2014-01-01T01:15:00        0.334152\n",
       "   7 │ 2014-01-01T01:30:00 \u001b[90m missing         \u001b[0m\n",
       "   8 │ 2014-01-01T01:45:00 \u001b[90m missing         \u001b[0m\n",
       "   9 │ 2014-01-01T02:00:00        0.0991336\n",
       "  10 │ 2014-01-01T02:15:00        0.125287"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using DataFrames\n",
    "using Dates\n",
    "using Random\n",
    "ENV[\"COLUMNS\"]=1000 # for dataframe column size\n",
    "using TSML: fit, fit!, transform, transform!\n",
    "using Plots\n",
    "\n",
    "function generateXY()\n",
    "    Random.seed!(123)\n",
    "    gdate = DateTime(2014,1,1):Dates.Minute(15):DateTime(2014,1,5)\n",
    "    gval = Array{Union{Missing,Float64}}(rand(length(gdate)))\n",
    "    gmissing = floor(0.30*length(gdate)) |> Integer\n",
    "    gndxmissing = Random.shuffle(1:length(gdate))[1:gmissing]\n",
    "    X = DataFrame(Date=gdate,Value=gval)\n",
    "    X.Value[gndxmissing] .= missing\n",
    "    Y = rand(length(gdate))\n",
    "    (X,Y)\n",
    "end;\n",
    "(df,outY)=generateXY(); first(df,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's load the TSML modules and filters to process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "using TSML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's use Pipeline with Plotter filter to plot artificial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pltr=Plotter(Dict(:pdfoutput => true))\n",
    "\n",
    "mypipeline = pltr\n",
    "\n",
    "fit_transform!(mypipeline, df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's get the statistics/data quality including blocks of missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>1×20 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">tstart</th><th style = \"text-align: left;\">tend</th><th style = \"text-align: left;\">sfreq</th><th style = \"text-align: left;\">count</th><th style = \"text-align: left;\">max</th><th style = \"text-align: left;\">min</th><th style = \"text-align: left;\">median</th><th style = \"text-align: left;\">mean</th><th style = \"text-align: left;\">q1</th><th style = \"text-align: left;\">q2</th><th style = \"text-align: left;\">q25</th><th style = \"text-align: left;\">q75</th><th style = \"text-align: left;\">q8</th><th style = \"text-align: left;\">q9</th><th style = \"text-align: left;\">kurtosis</th><th style = \"text-align: left;\">skewness</th><th style = \"text-align: left;\">variation</th><th style = \"text-align: left;\">entropy</th><th style = \"text-align: left;\">autocor</th><th style = \"text-align: left;\">pacf</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"DateTime\" style = \"text-align: left;\">DateTime</th><th title = \"DateTime\" style = \"text-align: left;\">DateTime</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">2014-01-01T00:00:00</td><td style = \"text-align: left;\">2014-01-05T00:00:00</td><td style = \"text-align: right;\">0.249351</td><td style = \"text-align: right;\">270</td><td style = \"text-align: right;\">0.999721</td><td style = \"text-align: right;\">0.000981938</td><td style = \"text-align: right;\">0.47879</td><td style = \"text-align: right;\">0.481656</td><td style = \"text-align: right;\">0.0787459</td><td style = \"text-align: right;\">0.157719</td><td style = \"text-align: right;\">0.230357</td><td style = \"text-align: right;\">0.728315</td><td style = \"text-align: right;\">0.778832</td><td style = \"text-align: right;\">0.883753</td><td style = \"text-align: right;\">-1.22101</td><td style = \"text-align: right;\">0.0106693</td><td style = \"text-align: right;\">0.602218</td><td style = \"text-align: right;\">67.6642</td><td style = \"text-align: right;\">0.25577</td><td style = \"text-align: right;\">0.275333</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccccccccccccccccccc}\n",
       "\t& tstart & tend & sfreq & count & max & min & median & mean & q1 & q2 & q25 & q75 & q8 & q9 & kurtosis & skewness & variation & entropy & autocor & pacf\\\\\n",
       "\t\\hline\n",
       "\t& DateTime & DateTime & Float64 & Int64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 2014-01-01T00:00:00 & 2014-01-05T00:00:00 & 0.249351 & 270 & 0.999721 & 0.000981938 & 0.47879 & 0.481656 & 0.0787459 & 0.157719 & 0.230357 & 0.728315 & 0.778832 & 0.883753 & -1.22101 & 0.0106693 & 0.602218 & 67.6642 & 0.25577 & 0.275333 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m1×20 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m tstart              \u001b[0m\u001b[1m tend                \u001b[0m\u001b[1m sfreq    \u001b[0m\u001b[1m count \u001b[0m\u001b[1m max      \u001b[0m\u001b[1m min         \u001b[0m\u001b[1m median  \u001b[0m\u001b[1m mean     \u001b[0m\u001b[1m q1        \u001b[0m\u001b[1m q2       \u001b[0m\u001b[1m q25      \u001b[0m\u001b[1m q75      \u001b[0m\u001b[1m q8       \u001b[0m\u001b[1m q9       \u001b[0m\u001b[1m kurtosis \u001b[0m\u001b[1m skewness  \u001b[0m\u001b[1m variation \u001b[0m\u001b[1m entropy \u001b[0m\u001b[1m autocor \u001b[0m\u001b[1m pacf     \u001b[0m\n",
       "     │\u001b[90m DateTime            \u001b[0m\u001b[90m DateTime            \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64     \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64   \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64   \u001b[0m\u001b[90m Float64   \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64  \u001b[0m\n",
       "─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
       "   1 │ 2014-01-01T00:00:00  2014-01-05T00:00:00  0.249351    270  0.999721  0.000981938  0.47879  0.481656  0.0787459  0.157719  0.230357  0.728315  0.778832  0.883753  -1.22101  0.0106693   0.602218  67.6642  0.25577  0.275333"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statfier = Statifier(Dict(:processmissing=>false))\n",
    "\n",
    "mypipeline = statfier\n",
    "\n",
    "fit_transform!(mypipeline, df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's extend the Pipeline workflow with aggregate, impute, and plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valgator = DateValgator(Dict(:dateinterval=>Dates.Hour(1)))\n",
    "\n",
    "mypipeline = valgator |> pltr\n",
    "\n",
    "fit_transform!(mypipeline, df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's now try real data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "fname = joinpath(dirname(pathof(TSML)),\"../data/testdata.csv\")\n",
    "csvreader = CSVDateValReader(Dict(:filename=>fname,:dateformat=>\"dd/mm/yyyy HH:MM\"))\n",
    "\n",
    "outputname = joinpath(dirname(pathof(TSML)),\"/tmp/testdata_output.csv\")\n",
    "csvwriter = CSVDateValWriter(Dict(:filename=>outputname))\n",
    "\n",
    "valgator = DateValgator(Dict(:dateinterval=>Dates.Hour(1)))\n",
    "valputer = DateValLinearImputer(Dict(:dateinterval=>Dates.Hour(1)))\n",
    "stfier = Statifier(Dict(:processmissing=>true))\n",
    "outliernicer = Outliernicer(Dict(:dateinterval=>Dates.Hour(1)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's plot the real data and check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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1380.34,795.858 1380.53,824.618 1380.73,824.618 1380.92,824.618 1381.12,834.204 1381.31,824.618 1381.51,824.618 1381.71,776.685 1381.9,728.752 1382.1,699.992 1382.68,671.233 1382.88,685.613 1383.08,680.819 1383.27,690.406 1383.47,695.199 1383.66,728.752 1383.86,728.752 1384.05,743.132 1384.25,743.132 1384.45,776.685 1385.23,776.685 1385.42,795.858 1385.62,824.618 1385.82,824.618 1386.01,834.204 1386.21,824.618 1386.4,776.685 1386.6,728.752 1386.79,671.233 1386.99,632.887 1387.38,584.954 1387.58,594.54 1387.77,632.887 1387.97,632.887 1388.16,680.819 1388.36,728.752 1388.56,738.339 1388.75,738.339 1388.95,747.925 1389.14,747.925 1389.73,824.618 1389.93,824.618 1390.12,824.618 1390.32,858.171 1390.51,824.618 1390.71,834.204 1390.9,795.858 1391.1,776.685 1391.3,728.752 1391.49,728.752 1392.08,642.473 1392.27,652.06 1392.47,632.887 1392.67,680.819 1392.86,690.406 1393.06,728.752 1393.25,728.752 1393.45,743.132 1393.64,776.685 1393.84,781.478 1394.43,824.618 1394.62,829.411 1394.82,872.55 1395.01,843.791 1395.21,838.997 1395.41,786.271 1395.6,776.685 1395.8,757.512 1395.99,680.819 1396.19,680.819 1396.78,656.853 1396.97,632.887 1397.17,632.887 1397.36,647.266 1397.56,695.199 1397.75,747.925 1397.95,776.685 1398.15,776.685 1398.34,795.858 1398.54,795.858 1399.32,858.171 1399.52,838.997 1399.71,786.271 1399.91,776.685 1400.1,786.271 1400.3,747.925 1400.49,738.339 1400.69,728.752 1400.89,699.992 1401.47,584.954 1401.67,594.54 1401.86,594.54 1402.06,632.887 1402.26,652.06 1402.45,680.819 1402.65,685.613 1402.84,690.406 1403.04,690.406 1403.23,704.786 1404.02,695.199 1404.21,728.752 1404.41,714.372 1404.6,709.579 1404.8,709.579 1405,680.819 1405.19,680.819 1405.39,652.06 1405.58,632.887 1406.37,594.54 1406.56,594.54 1406.76,632.887 1406.95,642.473 1407.15,642.473 1407.34,642.473 1407.54,642.473 1407.74,652.06 1407.93,632.887 1408.91,690.406 1409.11,714.372 1409.3,728.752 1409.5,728.752 1409.69,728.752 1409.89,680.819 1410.08,642.473 1410.28,652.06 1410.48,632.887 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1433.76,733.545 1899.76,920.483 1899.95,920.483 1900.15,920.483 1900.34,930.07 1900.54,920.483 1900.74,930.07 1900.93,920.483 1901.13,930.07 1901.32,930.07 \"/>\n",
       "<path clip-path=\"url(#clip630)\" d=\"M196.023 185.813 L430.184 185.813 L430.184 82.1328 L196.023 82.1328  Z\" fill=\"#ffffff\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
       "<polyline clip-path=\"url(#clip630)\" style=\"stroke:#000000; stroke-linecap:round; stroke-linejoin:round; stroke-width:4; stroke-opacity:1; fill:none\" points=\"196.023,185.813 430.184,185.813 430.184,82.1328 196.023,82.1328 196.023,185.813 \"/>\n",
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       "<path clip-path=\"url(#clip630)\" d=\"M371.404 153.66 Q369.598 158.29 367.885 159.702 Q366.172 161.114 363.302 161.114 L359.899 161.114 L359.899 157.549 L362.399 157.549 Q364.158 157.549 365.131 156.716 Q366.103 155.882 367.283 152.781 L368.047 150.836 L357.561 125.327 L362.075 125.327 L370.177 145.605 L378.279 125.327 L382.793 125.327 L371.404 153.66 Z\" fill=\"#000000\" fill-rule=\"nonzero\" fill-opacity=\"1\" /><path clip-path=\"url(#clip630)\" d=\"M390.084 147.318 L397.723 147.318 L397.723 120.952 L389.413 122.619 L389.413 118.359 L397.677 116.693 L402.353 116.693 L402.353 147.318 L409.992 147.318 L409.992 151.253 L390.084 151.253 L390.084 147.318 Z\" fill=\"#000000\" fill-rule=\"nonzero\" fill-opacity=\"1\" /></svg>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpipeline1 = csvreader |> valgator |> pltr\n",
    "\n",
    "fit_transform!(mpipeline1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's get the statistics to assess data quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>1×26 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">tstart</th><th style = \"text-align: left;\">tend</th><th style = \"text-align: left;\">sfreq</th><th style = \"text-align: left;\">count</th><th style = \"text-align: left;\">max</th><th style = \"text-align: left;\">min</th><th style = \"text-align: left;\">median</th><th style = \"text-align: left;\">mean</th><th style = \"text-align: left;\">q1</th><th style = \"text-align: left;\">q2</th><th style = \"text-align: left;\">q25</th><th style = \"text-align: left;\">q75</th><th style = \"text-align: left;\">q8</th><th style = \"text-align: left;\">q9</th><th style = \"text-align: left;\">kurtosis</th><th style = \"text-align: left;\">skewness</th><th style = \"text-align: left;\">variation</th><th style = \"text-align: left;\">entropy</th><th style = \"text-align: left;\">autocor</th><th style = \"text-align: left;\">pacf</th><th style = \"text-align: left;\">bmedian</th><th style = \"text-align: left;\">bmean</th><th style = \"text-align: left;\">bq25</th><th style = \"text-align: left;\">bq75</th><th style = \"text-align: left;\">bmin</th><th style = \"text-align: left;\">bmax</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"DateTime\" style = \"text-align: left;\">DateTime</th><th title = \"DateTime\" style = \"text-align: left;\">DateTime</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">2014-01-01T00:00:00</td><td style = \"text-align: left;\">2014-12-31T15:00:00</td><td style = \"text-align: right;\">0.998859</td><td style = \"text-align: right;\">3830</td><td style = \"text-align: right;\">18.8</td><td style = \"text-align: right;\">8.5</td><td style = \"text-align: right;\">10.35</td><td style = \"text-align: right;\">11.557</td><td style = \"text-align: right;\">9.9</td><td style = \"text-align: right;\">10.0</td><td style = \"text-align: right;\">10.0</td><td style = \"text-align: right;\">12.3</td><td style = \"text-align: right;\">13.0</td><td style = \"text-align: right;\">16.0</td><td style = \"text-align: right;\">0.730635</td><td style = \"text-align: right;\">1.41283</td><td style = \"text-align: right;\">0.200055</td><td style = \"text-align: right;\">-1.09145e5</td><td style = \"text-align: right;\">4.39315</td><td style = \"text-align: right;\">1.04644</td><td style = \"text-align: right;\">4931.0</td><td style = \"text-align: right;\">4931.0</td><td style = \"text-align: right;\">4931.0</td><td style = \"text-align: right;\">4931.0</td><td style = \"text-align: right;\">4931.0</td><td style = \"text-align: right;\">4931.0</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccccccccccccccccccccccccc}\n",
       "\t& tstart & tend & sfreq & count & max & min & median & mean & q1 & q2 & q25 & q75 & q8 & q9 & kurtosis & skewness & variation & entropy & autocor & pacf & bmedian & bmean & bq25 & bq75 & bmin & bmax\\\\\n",
       "\t\\hline\n",
       "\t& DateTime & DateTime & Float64 & Int64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 2014-01-01T00:00:00 & 2014-12-31T15:00:00 & 0.998859 & 3830 & 18.8 & 8.5 & 10.35 & 11.557 & 9.9 & 10.0 & 10.0 & 12.3 & 13.0 & 16.0 & 0.730635 & 1.41283 & 0.200055 & -1.09145e5 & 4.39315 & 1.04644 & 4931.0 & 4931.0 & 4931.0 & 4931.0 & 4931.0 & 4931.0 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m1×26 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m tstart              \u001b[0m\u001b[1m tend                \u001b[0m\u001b[1m sfreq    \u001b[0m\u001b[1m count \u001b[0m\u001b[1m max     \u001b[0m\u001b[1m min     \u001b[0m\u001b[1m median  \u001b[0m\u001b[1m mean    \u001b[0m\u001b[1m q1      \u001b[0m\u001b[1m q2      \u001b[0m\u001b[1m q25     \u001b[0m\u001b[1m q75     \u001b[0m\u001b[1m q8      \u001b[0m\u001b[1m q9      \u001b[0m\u001b[1m kurtosis \u001b[0m\u001b[1m skewness \u001b[0m\u001b[1m variation \u001b[0m\u001b[1m entropy    \u001b[0m\u001b[1m autocor \u001b[0m\u001b[1m pacf    \u001b[0m\u001b[1m bmedian \u001b[0m\u001b[1m bmean   \u001b[0m\u001b[1m bq25    \u001b[0m\u001b[1m bq75    \u001b[0m\u001b[1m bmin    \u001b[0m\u001b[1m bmax    \u001b[0m\n",
       "     │\u001b[90m DateTime            \u001b[0m\u001b[90m DateTime            \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64   \u001b[0m\u001b[90m Float64    \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n",
       "─────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
       "   1 │ 2014-01-01T00:00:00  2014-12-31T15:00:00  0.998859   3830     18.8      8.5    10.35   11.557      9.9     10.0     10.0     12.3     13.0     16.0  0.730635   1.41283   0.200055  -1.09145e5  4.39315  1.04644   4931.0   4931.0   4931.0   4931.0   4931.0   4931.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpipeline1 = csvreader |> valgator |> stfier \n",
    "\n",
    "fit_transform!(mpipeline1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's try imputing and verify the statistical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "<div><div style = \"float: left;\"><span>1×20 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">tstart</th><th style = \"text-align: left;\">tend</th><th style = \"text-align: left;\">sfreq</th><th style = \"text-align: left;\">count</th><th style = \"text-align: left;\">max</th><th style = \"text-align: left;\">min</th><th style = \"text-align: left;\">median</th><th style = \"text-align: left;\">mean</th><th style = \"text-align: left;\">q1</th><th style = \"text-align: left;\">q2</th><th style = \"text-align: left;\">q25</th><th style = \"text-align: left;\">q75</th><th style = \"text-align: left;\">q8</th><th style = \"text-align: left;\">q9</th><th style = \"text-align: left;\">kurtosis</th><th style = \"text-align: left;\">skewness</th><th style = \"text-align: left;\">variation</th><th style = \"text-align: left;\">entropy</th><th style = \"text-align: left;\">autocor</th><th style = \"text-align: left;\">pacf</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"DateTime\" style = \"text-align: left;\">DateTime</th><th title = \"DateTime\" style = \"text-align: left;\">DateTime</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th><th title = \"Float64\" style = \"text-align: left;\">Float64</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: left;\">2014-01-01T00:00:00</td><td style = \"text-align: left;\">2014-12-31T15:00:00</td><td style = \"text-align: right;\">0.998859</td><td style = \"text-align: right;\">8761</td><td style = \"text-align: right;\">18.8</td><td style = \"text-align: right;\">8.5</td><td style = \"text-align: right;\">9.9</td><td style = \"text-align: right;\">10.6244</td><td style = \"text-align: right;\">9.9</td><td style = \"text-align: right;\">9.9</td><td style = \"text-align: right;\">9.9</td><td style = \"text-align: right;\">10.0</td><td style = \"text-align: right;\">10.5</td><td style = \"text-align: right;\">12.5</td><td style = \"text-align: right;\">6.67036</td><td style = \"text-align: right;\">2.7373</td><td style = \"text-align: right;\">0.163356</td><td style = \"text-align: right;\">-2.21059e5</td><td style = \"text-align: right;\">4.50868</td><td style = \"text-align: right;\">1.05318</td></tr></tbody></table></div>"
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       "\\begin{tabular}{r|cccccccccccccccccccc}\n",
       "\t& tstart & tend & sfreq & count & max & min & median & mean & q1 & q2 & q25 & q75 & q8 & q9 & kurtosis & skewness & variation & entropy & autocor & pacf\\\\\n",
       "\t\\hline\n",
       "\t& DateTime & DateTime & Float64 & Int64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 2014-01-01T00:00:00 & 2014-12-31T15:00:00 & 0.998859 & 8761 & 18.8 & 8.5 & 9.9 & 10.6244 & 9.9 & 9.9 & 9.9 & 10.0 & 10.5 & 12.5 & 6.67036 & 2.7373 & 0.163356 & -2.21059e5 & 4.50868 & 1.05318 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m1×20 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m tstart              \u001b[0m\u001b[1m tend                \u001b[0m\u001b[1m sfreq    \u001b[0m\u001b[1m count \u001b[0m\u001b[1m max     \u001b[0m\u001b[1m min     \u001b[0m\u001b[1m median  \u001b[0m\u001b[1m mean    \u001b[0m\u001b[1m q1      \u001b[0m\u001b[1m q2      \u001b[0m\u001b[1m q25     \u001b[0m\u001b[1m q75     \u001b[0m\u001b[1m q8      \u001b[0m\u001b[1m q9      \u001b[0m\u001b[1m kurtosis \u001b[0m\u001b[1m skewness \u001b[0m\u001b[1m variation \u001b[0m\u001b[1m entropy    \u001b[0m\u001b[1m autocor \u001b[0m\u001b[1m pacf    \u001b[0m\n",
       "     │\u001b[90m DateTime            \u001b[0m\u001b[90m DateTime            \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64  \u001b[0m\u001b[90m Float64   \u001b[0m\u001b[90m Float64    \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n",
       "─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
       "   1 │ 2014-01-01T00:00:00  2014-12-31T15:00:00  0.998859   8761     18.8      8.5      9.9  10.6244      9.9      9.9      9.9     10.0     10.5     12.5   6.67036    2.7373   0.163356  -2.21059e5  4.50868  1.05318"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpipeline2 = csvreader |> valgator |> valputer |> statfier\n",
    "\n",
    "fit_transform!(mpipeline2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's visualize the imputted data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpipeline2 = csvreader |> valgator |> valputer |> pltr\n",
    "\n",
    "fit_transform!(mpipeline2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's have examples of Monotonic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "regularfile = joinpath(dirname(pathof(TSML)),\"../data/typedetection/regular.csv\")\n",
    "monofile = joinpath(dirname(pathof(TSML)),\"../data/typedetection/monotonic.csv\")\n",
    "dailymonofile = joinpath(dirname(pathof(TSML)),\"../data/typedetection/dailymonotonic.csv\")\n",
    "\n",
    "regularfilecsv = CSVDateValReader(Dict(:filename=>regularfile,:dateformat=>\"dd/mm/yyyy HH:MM\"))\n",
    "monofilecsv = CSVDateValReader(Dict(:filename=>monofile,:dateformat=>\"dd/mm/yyyy HH:MM\"))\n",
    "dailymonofilecsv = CSVDateValReader(Dict(:filename=>dailymonofile,:dateformat=>\"dd/mm/yyyy HH:MM\"))\n",
    "\n",
    "valgator = DateValgator(Dict(:dateinterval=>Dates.Hour(1)))\n",
    "valputer = DateValLinearImputer(Dict(:dateinterval=>Dates.Hour(1)))\n",
    "stfier = Statifier(Dict(:processmissing=>true))\n",
    "mononicer = Monotonicer(Dict())\n",
    "stfier = Statifier(Dict(:processmissing=>true))\n",
    "outliernicer = Outliernicer(Dict(:dateinterval=>Dates.Hour(1)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's plot an example of monotonic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monopipeline = monofilecsv |> valgator  |> pltr\n",
    "\n",
    "fit_transform!(monopipeline);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's plot after normalizing the monotonic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monopipeline = monofilecsv |> valgator |> valputer |> mononicer |> pltr\n",
    "\n",
    "fit_transform!(monopipeline);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's remove outliers and plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monopipeline = monofilecsv |> valgator |> valputer|> mononicer |> outliernicer |> pltr\n",
    "\n",
    "fit_transform!(monopipeline);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's plot and example of a daily monotonic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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1158.4,969.083 1159.46,951.8 1191.38,832.262 1192.44,815.843 1193.5,791.648 1194.57,764.284 1195.63,742.536 1196.69,723.093 1197.76,696.017 1198.82,672.398 1199.89,653.531 1200.95,628.039 1202.01,607.3 1203.08,587.137 1204.14,571.006 1205.2,1045.13 1206.27,1021.51 1207.33,1001.63 1208.4,980.605 1209.46,953.529 1210.52,920.691 1211.59,898.512 1212.65,865.963 1213.72,839.751 1214.78,812.387 1215.84,793.088 1216.91,762.843 1217.97,728.854 1219.03,704.658 1220.1,671.821 1221.16,639.705 1222.23,610.756 1223.29,589.729 1224.35,562.653 1225.42,542.778 1226.48,519.302 1227.54,498.419 1228.61,474.655 1229.67,1056.5 1230.74,1041.81 1231.8,1018.91 1232.86,994.287 1233.93,972.827 1234.99,954.681 1236.06,931.637 1237.12,914.066 1238.18,891.599 1239.25,872.588 1240.31,849.257 1241.37,827.221 1242.44,805.042 1243.5,781.854 1244.57,757.659 1245.63,730.726 1246.69,708.403 1247.76,681.903 1248.82,658.283 1249.88,633.512 1250.95,617.381 1252.01,598.37 1253.08,576.479 1254.14,557.468 1255.2,545.37 1256.27,1042.53 1257.33,1021.79 1258.4,1002.64 1259.46,981.757 1260.52,960.73 1261.59,938.262 1262.65,920.98 1263.71,898.8 1264.78,879.213 1265.84,857.898 1266.91,838.599 1267.97,814.403 1269.03,794.384 1270.1,772.061 1271.16,755.642 1272.22,732.311 1273.29,708.403 1274.35,692.273 1275.42,673.55 1276.48,643.881 1277.54,617.381 1278.61,587.713 1279.67,561.213 1280.74,1050.89 1281.8,1037.93 1282.86,1010.56 1283.93,979.164 1284.99,952.232 1286.05,930.485 1287.12,901.393 1288.18,873.452 1289.25,844.792 1290.31,821.892 1291.37,794.672 1292.44,762.987 1293.5,734.471 1294.56,712.148 1295.63,683.055 1296.69,662.316 1297.76,641.145 1298.82,618.245 1299.88,589.441 1300.95,572.446 1327.54,575.039 1328.61,555.452 1329.67,527.799 1330.73,492.514 1331.8,1052.9 1332.86,1036.34 1333.93,1009.99 1334.99,979.596 1336.05,957.561 1337.12,939.99 1338.18,916.659 1339.24,893.039 1340.31,874.46 1341.37,857.322 1342.44,840.759 1343.5,830.246 1344.56,813.971 1345.63,795.392 1346.69,777.39 1347.76,761.115 1348.82,739.8 1349.88,718.197 1350.95,697.169 1352.01,672.974 1353.07,647.05 1354.14,613.637 1355.2,586.128 1356.27,561.789 1366.9,794.816 1367.97,780.414 1369.03,754.202 1370.1,729.142 1371.16,712.148 1372.22,694.865 1373.29,676.142 1374.35,657.131 1375.41,636.68 1376.48,623.718 1377.54,604.419 1378.61,587.713 1379.67,571.87 1380.73,554.444 1381.8,535.721 1382.86,518.006 1383.92,499.859 1384.99,479.408 1386.05,462.413 1387.12,440.522 1388.18,425.256 1389.24,405.957 1390.31,384.354 1391.37,364.911 1392.44,349.788 1393.5,329.337 1394.56,313.207 1395.63,295.636 1396.69,280.082 1397.75,258.766 1398.82,239.467 1399.88,221.897 1400.95,194.965 1402.01,177.97 1403.07,159.967 1404.14,144.413 1405.2,127.13 1406.27,103.223 1407.33,87.6682 1408.39,76.8665 1409.46,1045.99 1410.52,1025.25 1411.58,1009.99 1412.65,991.838 1413.71,970.811 1414.78,952.376 1415.84,935.67 1416.9,915.507 1417.97,895.344 1419.03,880.941 1420.09,864.811 1421.16,847.528 1422.22,816.276 1423.29,790.784 1424.35,758.955 1425.41,723.381 1426.48,693.137 1427.54,666.493 1428.61,641.145 1429.67,618.821 1430.73,604.131 1431.8,590.305 1432.86,572.878 1433.92,560.348 1434.99,1048.58 1436.05,1028.42 1437.12,1008.26 1438.18,989.102 1439.24,966.779 1440.31,952.376 1441.37,932.933 1442.43,914.066 1443.5,897.648 1444.56,874.892 1445.63,860.346 1446.69,840.903 1447.75,824.197 1448.82,798.993 1449.88,769.18 1450.95,742.968 1452.01,719.925 1453.07,688.816 1454.14,669.517 1455.2,652.523 1456.26,634.376 1457.33,610.18 1458.39,595.202 1459.46,584.832 1460.52,1043.97 1461.58,1025.25 1462.65,1006.53 1463.71,988.382 1464.77,971.099 1465.84,950.648 1466.9,934.23 1467.97,913.778 1469.03,893.903 1470.09,871.148 1471.16,856.17 1472.22,841.191 1473.29,826.213 1474.35,803.314 1475.41,791.648 1476.48,777.102 1477.54,760.395 1478.6,743.544 1479.67,728.566 1480.73,705.523 1481.8,684.783 1482.86,668.653 1483.92,654.251 1484.99,1046.57 1486.05,1033.03 1487.11,1023.52 1488.18,1009.12 1489.24,992.126 1490.31,973.115 1491.37,956.697 1492.43,937.974 1493.5,920.115 1494.56,899.808 1495.63,878.925 1496.69,863.659 1497.75,846.088 1498.82,828.229 1499.88,808.93 1500.94,787.183 1502.01,763.131 1503.07,747.289 1504.14,733.751 1505.2,717.62 1506.26,696.881 1507.33,677.294 1508.39,660.012 1509.45,641.289 1510.52,1056.65 1511.58,1045.41 1512.65,1027.12 1513.71,1007.39 1514.77,987.23 1515.84,970.091 1516.9,949.784 1517.97,930.053 1519.03,907.73 1520.09,889.007 1521.16,869.852 1522.22,852.713 1523.28,838.311 1524.35,819.876 1525.41,801.729 1526.48,787.615 1527.54,770.333 1528.6,752.474 1529.67,735.623 1530.73,721.941 1531.79,699.762 1532.86,681.615 1533.92,664.764 1534.99,644.025 1536.05,1055.64 1537.11,1044.26 1538.18,1030.44 1539.24,1012.15 1540.31,990.542 1541.37,973.115 1542.43,955.545 1543.5,935.958 1544.56,918.387 1545.62,900.24 1546.69,879.357 1547.75,861.931 1548.82,848.68 1549.88,842.632 1552.01,842.055 1554.13,841.623 1555.2,841.335 1559.45,840.759 1560.52,840.039 1561.58,839.607 1563.71,1063.99 1565.84,1063.27 1566.9,1062.84 1567.96,1062.55 1569.03,1062.12 1573.28,1061.26 1574.35,1060.54 1577.54,1060.1 1580.73,1059.53 1583.92,1058.95 1584.99,1058.52 1587.11,1061.4 1588.18,1064.14 1589.24,1063.71 1591.37,1063.42 1596.69,1062.7 1604.13,1061.4 1605.2,1060.54 1607.33,1060.25 1609.45,1059.82 1610.52,1059.38 1612.64,1058.95 1614.77,1063.99 1615.84,1063.42 1617.96,1062.84 1619.03,1062.27 1620.09,1061.98 1621.16,1061.69 1622.22,1061.4 1623.28,1060.97 1628.6,1060.1 1630.73,1059.38 1636.05,1058.66 1638.18,1061.26 1643.5,1063.71 1646.69,1062.7 1650.94,1061.98 1654.13,1061.26 1657.32,1060.68 1663.71,1061.69 1665.84,1062.7 1666.9,1062.12 1667.96,1061.69 1669.03,1061.4 1670.09,1061.26 1671.15,1060.68 1678.6,1059.67 1680.73,1058.52 1682.86,1058.09 1686.05,1057.51 1688.18,1057.08 1689.24,1060.54 1695.62,1063.56 1696.69,1062.84 1697.75,1062.55 1699.88,1062.27 1703.07,1061.83 1704.13,1061.4 1710.52,1060.68 1713.71,1059.82 1714.77,1059.38 1715.83,1064.28 1717.96,1063.99 1719.03,1063.71 1721.15,1063.42 1725.41,1062.84 1727.54,1059.24 1728.6,1047.72 1729.66,1026.98 1730.73,1008.54 1731.79,982.909 1732.86,965.338 1733.92,952.664 1734.98,938.262 1736.05,924.148 1737.11,901.104 1738.17,886.702 1739.24,863.083 1740.3,854.441 1741.37,1045.7 1742.43,1028.85 1743.49,1008.54 1744.56,989.966 1745.62,971.675 1746.68,952.088 1747.75,929.909 1748.81,912.05 1749.88,893.039 1750.94,876.045 1752,853.865 1753.07,835.43 1754.13,821.892 1755.2,800.865 1756.26,774.941 1757.32,752.186 1758.39,731.447 1759.45,708.691 1760.51,684.495 1761.58,667.213 1762.64,644.745 1763.71,623.862 1764.77,599.234 1765.83,589.441 1766.9,1040.23 1767.96,1019.63 1769.02,1002.78 1770.09,982.333 1771.15,957.417 1772.22,933.653 1773.28,917.811 1774.34,894.768 1775.41,872.876 1776.47,853.577 1777.54,830.534 1778.6,815.555 1779.66,793.088 1780.73,782.718 1781.79,764.284 1782.85,738.072 1783.92,711.572 1784.98,696.305 1786.05,679.311 1787.11,661.452 1788.17,646.474 1789.24,625.446 1790.3,605.859 1791.36,1057.94 1792.43,1041.96 1793.49,1026.98 1794.56,1005.66 1795.62,989.534 1796.68,970.091 1797.75,952.664 1798.81,933.653 1799.88,915.795 1800.94,895.92 1802,878.493 1803.07,861.354 1804.13,850.121 1805.19,833.414 1806.26,817.572 1807.32,801.873 1808.39,785.023 1809.45,773.213 1810.51,755.066 1811.58,741.24 1812.64,728.71 1813.71,712.724 1814.77,687.376 1815.83,670.957 1816.9,658.427 1817.96,1045.56 1819.02,1029 1820.09,1009.41 1821.15,991.838 1822.22,967.931 1823.28,948.632 1824.34,936.246 1825.41,914.355 1826.47,893.327 1827.53,874.604 1828.6,860.058 1829.66,847.24 1830.73,833.126 1831.79,816.708 1832.85,805.474 1833.92,789.055 1834.98,776.525 1836.05,763.996 1837.11,749.593 1838.17,731.447 1839.24,715.892 1840.3,698.322 1841.36,677.87 1842.43,861.931 1843.49,1043.69 1844.56,1024.82 1845.62,1008.54 1846.68,988.67 1847.75,971.963 1848.81,952.664 1849.87,931.205 1850.94,912.626 1852,893.903 1853.07,878.061 1854.13,854.441 1855.19,838.887 1856.26,821.028 1857.32,801.153 1858.39,780.126 1859.45,765.148 1860.51,741.384 1861.58,726.262 1862.64,702.786 1863.7,687.952 1864.77,668.653 1865.83,650.794 1866.9,630.631 1867.96,614.213 1869.02,594.05 1870.09,574.751 1871.15,557.18 1872.21,539.609 1873.28,517.142 1874.34,499.571 1875.41,482.433 1876.47,463.854 1877.53,443.115 1878.6,424.968 1879.66,405.093 1880.73,387.234 1881.79,367.359 1882.85,347.484 1883.92,329.049 1884.98,306.582 1886.04,290.163 1887.11,274.033 1888.17,262.223 1889.24,244.364 1890.3,226.217 1891.36,211.239 1892.43,187.763 1893.49,177.538 1894.55,1042.82 1895.62,1027.56 1896.68,1008.26 1897.75,986.654 1898.81,971.963 1899.87,951.512 1900.94,934.086 1902,917.523 1903.07,908.738 249.901,908.738 254.157,908.738 259.476,908.738 263.731,908.738 267.986,908.738 272.241,908.738 276.497,908.738 656.278,908.738 658.405,908.738 659.469,908.738 660.533,908.738 661.597,908.738 662.66,908.738 663.724,908.738 664.788,908.738 669.043,908.738 670.107,908.738 672.235,908.738 673.299,908.738 674.362,908.738 676.49,908.738 678.618,908.738 679.681,908.738 680.745,908.738 681.809,908.738 682.873,908.738 683.937,908.738 685,908.738 688.192,908.738 690.32,908.738 691.383,908.738 692.447,908.738 695.639,908.738 696.702,908.738 697.766,908.738 698.83,908.738 699.894,908.738 700.958,908.738 703.085,908.738 704.149,908.738 706.277,908.738 707.341,908.738 708.404,908.738 710.532,908.738 711.596,908.738 712.66,908.738 713.723,908.738 714.787,908.738 715.851,908.738 716.915,908.738 720.106,908.738 722.234,908.738 723.298,908.738 724.362,908.738 727.553,908.738 728.617,908.738 730.744,908.738 731.808,908.738 732.872,908.738 733.936,908.738 735,908.738 738.191,908.738 739.255,908.738 742.446,908.738 746.702,908.738 747.765,908.738 748.829,908.738 749.893,908.738 752.021,908.738 753.084,908.738 754.148,908.738 756.276,908.738 757.34,908.738 759.467,908.738 760.531,908.738 762.659,908.738 763.723,908.738 764.786,908.738 766.914,908.738 767.978,908.738 772.233,908.738 773.297,908.738 774.361,908.738 775.425,908.738 779.68,908.738 780.744,908.738 782.871,908.738 783.935,908.738 786.063,908.738 788.19,908.738 789.254,908.738 793.509,908.738 799.892,908.738 800.956,908.738 802.02,908.738 806.275,908.738 807.339,908.738 811.594,908.738 812.658,908.738 813.722,908.738 817.977,908.738 819.041,908.738 820.105,908.738 822.232,908.738 823.296,908.738 824.36,908.738 825.424,908.738 826.487,908.738 829.679,908.738 830.743,908.738 831.807,908.738 876.487,908.738 877.55,908.738 878.614,908.738 879.678,908.738 880.742,908.738 942.443,908.738 944.571,908.738 946.698,908.738 947.762,908.738 950.954,908.738 955.209,908.738 957.336,908.738 961.592,908.738 964.783,908.738 965.847,908.738 966.911,908.738 967.974,908.738 970.102,908.738 972.23,908.738 973.294,908.738 976.485,908.738 977.549,908.738 979.676,908.738 980.74,908.738 983.932,908.738 984.995,908.738 987.123,908.738 988.187,908.738 989.251,908.738 990.315,908.738 991.378,908.738 992.442,908.738 993.506,908.738 994.57,908.738 996.697,908.738 997.761,908.738 1000.95,908.738 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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dailymonopipeline = dailymonofilecsv |> valgator |> valputer |> pltr\n",
    "\n",
    "fit_transform!(dailymonopipeline);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's normalize and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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475.896,1004.26 476.978,993.966 478.059,987.677 479.14,988.821 480.222,981.961 481.303,980.817 482.385,976.244 483.466,993.394 484.547,999.682 485.629,998.539 486.71,987.677 487.791,1009.97 488.873,977.959 489.954,1008.83 491.036,1001.97 492.117,1001.97 493.198,1001.97 494.28,972.242 495.361,1010.54 496.443,988.821 497.524,1000.25 498.605,1000.25 499.687,994.537 500.768,994.537 501.849,987.677 502.931,991.107 504.012,1005.97 505.094,983.104 506.175,978.531 507.256,989.392 508.338,981.389 509.419,992.822 510.501,993.966 511.582,1006.54 512.663,991.107 513.745,993.966 514.826,1002.54 515.908,992.822 516.989,1027.12 518.07,962.524 519.152,962.524 520.233,999.111 521.314,983.104 522.396,968.812 523.477,987.106 524.559,981.961 525.64,965.382 526.721,996.252 527.803,984.247 528.884,968.241 529.966,976.244 531.047,955.664 532.128,971.671 533.21,932.797 534.291,958.522 535.372,991.679 536.454,987.677 537.535,975.101 538.617,988.821 539.698,1000.25 540.779,988.821 541.861,975.101 542.942,1021.98 544.024,981.389 545.105,981.389 546.186,961.38 547.268,973.386 548.349,998.539 549.43,989.964 550.512,987.677 551.593,986.534 552.675,1000.25 553.756,991.107 554.837,983.104 555.919,1000.25 557,996.824 558.082,969.956 559.163,992.822 560.244,997.967 561.326,965.954 562.407,983.104 563.488,1003.68 564.57,1002.54 565.651,995.681 566.733,993.394 567.814,992.251 568.895,1026.55 569.977,980.246 571.058,980.246 572.14,1003.11 573.221,997.967 574.302,988.821 575.384,992.251 576.465,986.534 577.547,1005.97 578.628,997.967 579.709,985.391 580.791,1007.11 581.872,1003.11 582.953,966.526 584.035,972.814 585.116,1000.25 586.198,993.394 587.279,996.824 588.36,999.111 589.442,1000.25 590.523,982.532 591.605,1000.83 592.686,973.386 593.767,1008.83 594.849,1013.97 595.93,1013.97 597.011,986.534 598.093,1015.12 599.174,986.534 600.256,995.109 601.337,987.677 602.418,1003.11 603.5,999.111 604.581,981.961 605.663,1009.4 606.744,992.251 607.825,985.391 608.907,947.66 609.988,961.38 611.069,964.81 612.151,940.8 613.232,975.101 614.314,980.817 615.395,1000.83 616.476,999.111 617.558,1003.11 618.639,989.964 619.721,997.967 620.802,1031.12 621.883,1031.12 622.965,981.961 624.046,995.109 625.127,1004.26 626.209,993.394 627.29,992.822 628.372,989.392 629.453,1003.68 630.534,993.394 631.616,995.681 632.697,988.821 633.779,1005.4 635.941,1045.42 644.592,1059.71 645.674,1060.85 646.755,1063.14 649.999,1057.42 654.325,1057.42 656.488,1061.99 665.139,1059.71 666.22,1060.28 668.383,1063.14 672.708,1061.99 673.79,1060.85 681.36,1060.85 684.604,1060.28 688.929,1061.42 697.58,1059.14 698.662,1063.14 700.825,1063.14 705.15,1061.99 706.231,1061.99 709.476,1062.57 715.964,1060.85 717.045,1061.42 720.289,1062.57 721.371,1062.57 723.534,1063.14 724.615,1061.42 725.696,1061.42 731.103,1061.42 735.429,1060.85 738.673,1061.42 741.917,1061.99 746.243,1061.42 749.487,1061.42 750.568,1061.42 751.65,1061.42 757.057,1061.42 758.138,1061.99 759.219,1063.14 762.464,1062.57 765.708,1061.42 767.87,1061.42 771.115,1060.85 772.196,1060.85 773.277,1063.14 775.44,1061.99 776.522,1062.57 777.603,1062.57 778.684,1062.57 779.766,1063.14 784.091,1060.85 785.173,1060.28 786.254,1062.57 789.498,1060.28 790.58,1062.57 791.661,1063.14 795.986,1061.99 797.068,1061.99 798.149,1063.14 802.475,1057.42 808.963,1057.42 810.045,1059.71 814.37,1060.85 815.451,1045.42 816.533,1016.26 817.614,1007.69 818.696,1012.83 819.777,995.109 820.858,1013.97 821.94,1003.68 823.021,996.824 824.103,1007.11 825.184,989.964 826.265,1005.97 827.347,996.252 828.428,1024.84 829.509,1007.11 830.591,1007.11 831.672,1009.97 832.754,1013.97 833.835,995.681 834.916,1006.54 835.998,1016.83 837.079,1010.54 838.161,1008.26 839.242,1002.54 840.323,1004.83 841.405,1003.68 842.486,981.961 843.567,989.392 844.649,1006.54 845.73,1001.4 846.812,987.106 847.893,1015.69 848.974,992.251 850.056,992.251 851.137,989.964 852.219,1007.11 853.3,1009.4 854.381,1020.83 855.463,996.252 856.544,996.252 857.625,1014.55 864.114,642.387 865.195,1020.83 866.277,1000.25 867.358,1003.68 868.439,989.392 869.521,1004.26 870.602,1018.55 871.684,994.537 872.765,994.537 873.846,1013.97 874.928,991.679 876.009,998.539 877.09,996.824 878.172,1004.83 879.253,1001.4 880.335,1024.84 881.416,1001.97 882.497,1001.97 883.579,1000.83 884.66,1007.11 885.742,1007.11 886.823,989.392 887.904,999.682 888.986,998.539 890.067,1004.83 891.148,996.252 892.23,997.967 893.311,989.964 894.393,1017.4 895.474,996.824 896.555,1007.11 897.637,1026.55 898.718,994.537 899.8,1008.26 900.881,1012.83 901.962,1011.69 903.044,991.679 904.125,983.676 905.206,1013.97 906.288,1016.26 907.369,999.682 908.451,999.682 909.532,1008.26 910.613,1002.54 911.695,989.964 912.776,988.821 913.858,1025.41 914.939,979.102 916.02,983.676 917.102,978.531 918.183,967.097 919.264,983.104 920.346,964.81 921.427,992.251 922.509,980.817 923.59,967.097 924.671,1024.26 926.834,1057.42 928.997,1061.42 932.241,1061.42 933.323,1061.42 935.485,1061.42 936.567,1061.99 937.648,1062.57 939.811,1062.57 941.974,1061.42 943.055,1061.99 944.136,1063.14 946.299,1062.57 947.381,1062.57 952.787,1060.85 954.95,1059.71 958.194,1061.99 959.276,1061.99 962.52,1061.99 965.764,1060.28 966.845,1060.85 970.09,1061.42 979.822,1059.14 983.066,1059.71 984.148,1062.57 986.31,1062.57 987.392,1062.57 994.962,1060.85 997.124,1048.85 998.206,1007.11 999.287,970.527 1000.37,1021.98 1002.53,922.507 1003.61,981.961 1004.69,999.111 1005.78,1001.4 1006.86,964.81 1007.94,981.389 1009.02,1009.97 1010.1,988.821 1011.18,988.821 1012.26,988.821 1013.35,996.252 1014.43,985.391 1015.51,991.679 1016.59,979.674 1017.67,1003.68 1018.75,993.966 1019.83,975.672 1020.91,976.244 1022,996.824 1023.08,1003.68 1024.16,991.107 1025.24,1012.83 1026.32,1004.83 1027.4,1003.68 1028.48,986.534 1029.57,1007.11 1030.65,980.817 1031.73,975.672 1032.81,1000.83 1033.89,977.387 1034.97,987.677 1036.05,1019.69 1037.14,1019.69 1038.22,995.109 1039.3,1004.26 1040.38,991.107 1041.46,1010.54 1042.54,1008.26 1043.62,987.677 1044.71,996.824 1045.79,996.252 1046.87,1011.12 1047.95,993.394 1049.03,1004.83 1050.11,1004.83 1051.19,1005.97 1052.27,991.107 1053.36,1001.4 1054.44,992.251 1055.52,1003.68 1056.6,1001.4 1057.68,1002.54 1058.76,991.679 1059.84,982.532 1060.93,1011.69 1062.01,1009.97 1063.09,1009.97 1064.17,1006.54 1065.25,1000.83 1066.33,984.247 1067.41,1005.4 1068.5,998.539 1069.58,1011.69 1070.66,1005.97 1071.74,1010.54 1072.82,992.251 1073.9,1015.12 1074.98,996.824 1076.07,992.251 1077.15,1016.26 1078.23,1008.26 1079.31,989.964 1080.39,1011.69 1081.47,1004.26 1082.55,990.536 1083.64,978.531 1084.72,992.251 1085.8,993.394 1086.88,965.954 1087.96,1032.27 1089.04,1032.27 1090.12,970.527 1091.2,983.104 1092.29,1013.97 1093.37,997.967 1094.45,1000.25 1095.53,995.681 1096.61,999.111 1097.69,1001.4 1098.77,1000.25 1099.86,983.676 1100.94,1005.97 1102.02,992.822 1103.1,1003.68 1104.18,1009.4 1105.26,1011.69 1106.34,991.107 1107.43,980.817 1108.51,998.539 1109.59,985.962 1110.67,992.251 1111.75,973.957 1112.83,1010.54 1113.91,1005.4 1115,1005.4 1116.08,1012.26 1117.16,981.961 1118.24,1000.25 1119.32,985.391 1120.4,965.382 1121.48,1021.41 1122.57,993.394 1123.65,976.244 1124.73,995.681 1125.81,997.967 1126.89,981.961 1127.97,988.821 1129.05,1008.26 1130.13,1007.11 1131.22,977.387 1132.3,998.539 1133.38,1008.26 1134.46,985.962 1135.54,968.241 1136.62,1014.55 1137.7,984.819 1138.79,983.676 1139.87,1016.26 1140.95,1016.26 1142.03,988.821 1143.11,981.961 1144.19,1001.4 1145.27,985.391 1146.36,995.681 1178.8,589.794 1179.88,999.111 1180.96,968.241 1182.04,955.664 1183.12,977.959 1184.2,987.106 1185.29,956.807 1186.37,970.527 1187.45,989.392 1188.53,963.095 1189.61,981.961 1190.69,984.247 1191.77,1000.25 1192.86,970.527 1193.94,970.527 1195.02,985.391 1196.1,980.817 1197.18,956.807 1198.26,933.94 1199.34,976.244 1200.42,935.084 1201.51,960.237 1202.59,955.664 1203.67,987.677 1204.75,944.23 1205.83,929.367 1206.91,968.241 1207.99,933.94 1209.08,936.799 1210.16,949.375 1211.24,980.817 1212.32,956.807 1213.4,985.391 1214.48,971.099 1215.56,981.389 1216.65,969.956 1217.73,1005.97 1218.81,1005.97 1219.89,973.386 1220.97,966.526 1222.05,979.102 1223.13,992.251 1224.22,972.814 1225.3,994.537 1226.38,975.101 1227.46,988.821 1228.54,971.671 1229.62,976.816 1230.7,976.244 1231.79,972.242 1232.87,968.241 1233.95,957.379 1235.03,975.672 1236.11,959.094 1237.19,970.527 1238.27,965.954 1239.35,1000.25 1240.44,988.821 1241.52,977.387 1242.6,988.821 1243.68,1016.26 1244.76,981.961 1245.84,981.961 1246.92,988.249 1248.01,981.389 1249.09,980.817 1250.17,975.101 1251.25,995.681 1252.33,976.244 1253.41,986.534 1254.49,979.674 1255.58,987.677 1256.66,968.241 1257.74,984.819 1258.82,975.672 1259.9,999.111 1260.98,971.671 1262.06,969.384 1263.15,1000.25 1264.23,989.964 1265.31,946.517 1266.39,959.094 1267.47,946.517 1268.55,959.094 1269.63,1012.83 1270.71,1012.83 1271.8,955.664 1272.88,939.657 1273.96,957.379 1275.04,977.959 1276.12,948.804 1277.2,953.377 1278.28,950.519 1279.37,973.386 1280.45,956.235 1281.53,938.514 1282.61,951.09 1283.69,975.672 1284.77,948.804 1285.85,981.961 1286.94,980.246 1288.02,973.386 1289.1,949.947 1290.18,996.824 1317.21,986.534 1318.3,986.534 1319.38,954.52 1320.46,924.222 1321.54,998.539 1322.62,998.539 1323.7,959.665 1324.78,943.659 1325.87,976.816 1326.95,994.537 1328.03,971.671 1329.11,970.527 1330.19,990.536 1331.27,996.252 1332.35,998.539 1333.44,1022.55 1334.52,999.682 1335.6,990.536 1336.68,992.822 1337.76,999.682 1338.84,979.674 1339.92,978.531 1341.01,980.817 1342.09,968.241 1343.17,961.38 1344.25,931.654 1345.33,955.092 1346.41,967.669 1357.23,1007.11 1358.31,1007.11 1359.39,960.237 1360.47,964.81 1361.55,996.824 1362.63,995.681 1363.71,989.964 1364.8,988.821 1365.88,983.104 1366.96,1012.83 1368.04,987.677 1369.12,997.967 1370.2,1001.4 1371.28,995.109 1372.37,989.964 1373.45,993.966 1374.53,992.251 1375.61,983.104 1376.69,996.824 1377.77,977.387 1378.85,1003.68 1379.93,987.677 1381.02,978.531 1382.1,987.106 1383.18,1004.26 1384.26,983.104 1385.34,1000.25 1386.42,994.537 1387.5,1002.54 1388.59,979.674 1389.67,987.677 1390.75,994.537 1391.83,957.379 1392.91,996.824 1393.99,992.822 1395.07,1002.54 1396.16,995.681 1397.24,969.384 1398.32,1002.54 1399.4,1021.41 1400.48,981.961 1401.56,981.961 1402.64,1003.68 1403.73,992.251 1404.81,980.817 1405.89,991.107 1406.97,997.967 1408.05,984.247 1409.13,984.247 1410.21,1007.11 1411.3,1000.25 1412.38,995.681 1413.46,940.229 1414.54,963.095 1415.62,937.942 1416.7,923.078 1417.78,944.23 1418.86,958.522 1419.95,963.667 1421.03,975.672 1422.11,1005.97 1423.19,1009.4 1424.27,995.109 1425.35,1014.55 1426.43,984.247 1427.52,984.247 1428.6,984.247 1429.68,988.249 1430.76,975.672 1431.84,1007.11 1432.92,987.106 1434,989.392 1435.09,999.111 1436.17,973.957 1437.25,1006.54 1438.33,987.106 1439.41,997.967 1440.49,964.239 1441.57,945.945 1442.66,960.237 1443.74,972.814 1444.82,940.8 1445.9,987.677 1446.98,996.824 1448.06,992.251 1449.14,968.241 1450.23,1004.83 1451.31,1023.12 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dailymonopipeline = dailymonofilecsv |> valgator |> valputer |> mononicer |> pltr\n",
    "    \n",
    "fit_transform!(dailymonopipeline);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's add the Outliernicer filter and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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1132.05,674.093 1133.13,518.814 1134.22,395.387 1135.3,717.89 1136.38,510.851 1137.47,502.888 1138.55,729.834 1139.63,729.834 1140.72,538.722 1141.8,490.944 1142.88,626.315 1143.97,514.833 1145.05,586.5 1177.54,598.445 1178.63,610.389 1179.71,395.387 1180.79,307.794 1181.87,463.073 1182.96,526.777 1184.04,315.757 1185.12,411.314 1186.21,542.703 1187.29,359.554 1188.37,490.944 1189.46,506.87 1190.54,618.352 1191.62,411.314 1192.71,411.314 1193.79,514.833 1194.87,482.981 1195.96,315.757 1197.04,156.497 1198.12,451.129 1199.21,164.46 1200.29,339.646 1201.37,307.794 1202.45,530.759 1203.54,228.164 1204.62,124.645 1205.7,395.387 1206.79,156.497 1207.87,176.404 1208.95,263.998 1210.04,482.981 1211.12,315.757 1212.2,514.833 1213.29,415.295 1214.37,486.962 1215.45,407.332 1216.54,658.167 1217.62,658.167 1218.7,431.221 1219.78,383.443 1220.87,471.036 1221.95,562.611 1223.03,427.24 1224.12,578.537 1225.2,443.166 1226.28,538.722 1227.37,419.277 1228.45,455.11 1229.53,451.129 1230.62,423.258 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1403.92,482.981 1405,554.648 1406.08,602.426 1407.17,506.87 1408.25,506.87 1409.33,666.13 1410.42,618.352 1411.5,586.5 1412.58,200.293 1413.67,359.554 1414.75,184.367 1415.83,80.8481 1416.92,228.164 1418,327.702 1419.08,363.535 1420.17,447.147 1421.25,658.167 1422.33,682.056 1423.41,582.519 1424.5,717.89 1425.58,506.87 1426.66,506.87 1427.75,506.87 1428.83,534.74 1429.91,447.147 1431,666.13 1432.08,526.777 1433.16,542.703 1434.25,610.389 1435.33,435.203 1436.41,662.149 1437.5,526.777 1438.58,602.426 1439.66,367.517 1440.75,240.109 1441.83,339.646 1442.91,427.24 1443.99,204.275 1445.08,530.759 1446.16,594.463 1447.24,562.611 1448.33,395.387 1449.41,650.204 1450.49,777.613 1451.58,546.685 1452.66,546.685 1453.74,546.685 1454.83,562.611 1455.91,586.5 1456.99,498.907 1458.08,610.389 1459.16,498.907 1460.24,514.833 1461.32,435.203 1462.41,650.204 1463.49,650.204 1464.57,650.204 1465.66,431.221 1466.74,741.779 1467.82,662.149 1468.91,602.426 1469.99,598.445 1471.07,650.204 1472.16,427.24 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1541.48,697.982 1542.56,897.058 1544.73,1048.36 1546.89,1052.34 1547.98,1056.32 1552.31,1048.36 1553.39,1044.37 1554.47,1052.34 1556.64,1044.37 1558.81,1044.37 1559.89,1052.34 1560.97,1056.32 1562.06,1052.34 1566.39,1040.39 1567.47,1044.37 1570.72,1052.34 1573.97,1048.36 1577.22,1048.36 1578.3,1052.34 1580.47,1052.34 1581.55,1052.34 1582.64,1052.34 1584.8,1056.32 1590.22,1044.37 1597.8,1028.45 1598.88,1040.39 1601.05,1056.32 1603.22,1052.34 1604.3,1052.34 1606.47,1052.34 1608.63,1048.36 1609.71,1048.36 1611.88,1048.36 1612.96,1048.36 1614.05,1056.32 1615.13,1056.32 1616.21,1056.32 1617.3,1052.34 1622.71,1040.39 1624.88,1044.37 1630.29,1044.37 1632.46,1036.41 1637.88,1036.41 1641.13,1036.41 1645.46,1044.37 1648.71,1044.37 1651.96,1048.36 1658.46,1048.36 1660.62,1048.36 1661.71,1048.36 1662.79,1052.34 1663.87,1056.32 1664.95,1060.3 1666.04,1048.36 1673.62,1036.41 1675.79,1032.43 1677.95,1052.34 1681.2,1048.36 1683.37,1052.34 1684.45,1044.37 1690.95,1044.37 1692.03,1044.37 1693.12,1056.32 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1848.01,471.036 1849.09,550.666 1850.17,546.685 1851.25,626.315 1852.34,411.314 1853.42,634.278 1854.5,570.574 1855.59,514.833 1856.67,482.981 1857.75,650.204 1858.84,407.332 1859.92,646.223 1861,415.295 1862.09,654.186 1863.17,530.759 1864.25,570.574 1865.34,506.87 1866.42,610.389 1867.5,506.87 1868.59,530.759 1869.67,578.537 1870.75,578.537 1871.83,443.166 1872.92,578.537 1874,590.482 1875.08,550.666 1876.17,490.944 1877.25,562.611 1878.33,514.833 1879.42,570.574 1880.5,514.833 1881.58,514.833 1882.67,554.648 1883.75,443.166 1884.83,610.389 1885.92,618.352 1887,737.797 1888.08,570.574 1889.16,562.611 1890.25,650.204 1891.33,415.295 1892.41,781.594 1893.5,642.241 1894.58,642.241 1895.66,530.759 1896.75,467.055 1897.83,658.167 1898.91,498.907 1900,582.519 1901.08,606.408 1902.16,821.409 218.964,1064.28 223.297,1064.28 228.712,1064.28 233.045,1064.28 237.378,1064.28 241.71,1064.28 246.043,1064.28 632.723,1064.28 634.89,1064.28 635.973,1064.28 637.056,1064.28 638.139,1064.28 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dailymonopipeline = dailymonofilecsv |> valgator |> valputer |> mononicer |> outliernicer |> pltr\n",
    "\n",
    "fit_transform!(dailymonopipeline);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's use what we have learned so far to perform automatic data type classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getting stats of AirOffTemp1.csv\n",
      "getting stats of AirOffTemp2.csv\n",
      "getting stats of AirOffTemp3.csv\n",
      "getting stats of Energy1.csv\n",
      "getting stats of Energy10.csv\n",
      "getting stats of Energy2.csv\n",
      "getting stats of Energy3.csv\n",
      "getting stats of Energy4.csv\n",
      "getting stats of Energy6.csv\n",
      "getting stats of Energy7.csv\n",
      "getting stats of Energy8.csv\n",
      "getting stats of Energy9.csv\n",
      "getting stats of Pressure1.csv\n",
      "getting stats of Pressure3.csv\n",
      "getting stats of Pressure4.csv\n",
      "getting stats of Pressure6.csv\n",
      "getting stats of RetTemp11.csv\n",
      "skipping RetTemp21.csv: ErrorException(\"Nearest Neigbour algo failed to replace missings\")\n",
      "skipping RetTemp41.csv: ErrorException(\"Nearest Neigbour algo failed to replace missings\")\n",
      "getting stats of RetTemp51.csv\n",
      "getting stats of AirOffTemp4.csv\n",
      "getting stats of AirOffTemp5.csv\n",
      "getting stats of Energy5.csv\n",
      "getting stats of Pressure5.csv\n",
      "skipping RetTemp31.csv: ErrorException(\"Nearest Neigbour algo failed to replace missings\")\n",
      "loading model from file: /Users/ppalmes/phome/julia/TSML.jl/src/../data/realdatatsclassification/model/juliarfmodel.serialized\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "75.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using TSML: TSClassifier\n",
    "Random.seed!(12)\n",
    "\n",
    "trdirname = joinpath(dirname(pathof(TSML)),\"../data/realdatatsclassification/training\")\n",
    "tstdirname = joinpath(dirname(pathof(TSML)),\"../data/realdatatsclassification/testing\")\n",
    "modeldirname = joinpath(dirname(pathof(TSML)),\"../data/realdatatsclassification/model\")\n",
    "\n",
    "tscl = TSClassifier(Dict(:trdirectory=>trdirname,\n",
    "           :tstdirectory=>tstdirname,\n",
    "           :modeldirectory=>modeldirname,\n",
    "           :feature_range => 6:20,\n",
    "           :num_trees=>50)\n",
    ")\n",
    "\n",
    "fit!(tscl)\n",
    "dfresults = transform!(tscl);\n",
    "apredict = dfresults.predtype\n",
    "fnames = dfresults.fname\n",
    "myregex = r\"(?<dtype>[A-Z _ - a-z]+)(?<number>\\d*).(?<ext>\\w+)\"\n",
    "mtypes=map(fnames) do fname\n",
    "  mymatch=match(myregex,fname)\n",
    "  mymatch[:dtype]\n",
    "end\n",
    "\n",
    "sum(mtypes .== apredict)/length(mtypes) * 100 |> x-> round(x,digits=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## TSML features\n",
    "- TS data type clustering/classification for automatic data discovery\n",
    "- TS aggregation based on date/time interval\n",
    "- TS imputation based on symmetric Nearest Neighbors\n",
    "- TS statistical metrics for data quality assessment\n",
    "- TS ML wrapper with more than 100+ libraries from caret, scikitlearn, and julia\n",
    "- TS date/value matrix conversion of 1-D TS using sliding windows for ML input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## More TSML features\n",
    "- Common API wrappers for ML libs from JuliaML, PyCall, and RCall\n",
    "- Pipeline API allows high-level description of the processing workflow\n",
    "- Specific cleaning/normalization workflow based on data type\n",
    "- Automatic selection of optimised ML model\n",
    "- Automatic segmentation of time-series data into matrix form for ML training and prediction\n",
    "- Easily extensible architecture by using just two main interfaces: fit and transform\n",
    "- Meta-ensembles for robust prediction\n",
    "- Support for distributed computation, for scalability, and speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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