Time series utility library for Python.
Features:
- classes for evenly / unevenly spaced time series
- interpolation of unevenly spaced time series (interpolation example)
- transformation of evenly spaced time series into dataset (forecast example)
- time series forecast wrapper for e.g. keras (forecast example)
Requirements:
Python3.5+
Installation:
git clone https://github.com/kweimann/conflux.gitcd confluxpip install .
See examples/interpolation.py for complete example.
# number of observations
n = 25
# time interval i.e. first and last timestamp
t0, tn = [0, 200]
# function producing observation value from observation timestamp
f = lambda x: .00001 * (x - 100) * (x - 50) * (x - 150)
# 1000 values from time interval used for plotting
xs = np.linspace(t0, tn, 1000)
# randomly choose `n` timestamps from time interval
timestamps = np.sort(np.random.choice(np.arange(t0, tn + 1, 1), n, replace=False))
# calculate their respective observed values
observations = np.apply_along_axis(f, arr=timestamps, axis=0)
# create time series object for unevenly spaced time series
time_series = conflux.ts.IrregularTimeSeries(observations, timestamps)
# linearly interpolate the time series starting from `t0` until `tn`
linear_interp = time_series.interpolate(interval=1, start_timestamp=t0,
end_timestamp=tn, method="linear")
# interpolate time series using `most recent` strategy starting from `t0` until `tn`
most_recent_interp = time_series.interpolate(interval=1, start_timestamp=t0,
end_timestamp=tn, method="most_recent")
See examples/neural_network_forecast.py for complete example.
# use keras.Sequential's predict method as
# the implementation of Solver's abstract predict method
class NeuralNetwork(keras.models.Sequential, conflux.Solver):
pass
# time series: 0..199
data = np.arange(200, dtype=np.float32)
# number of input / output nodes in the neural network
n_in, n_out = 1, 1
# number of forecasting steps
steps_1, steps_2 = 20, 1
# create evenly spaced time series from the data
ts = conflux.ts.RegularTimeSeries(data, interval=1, start_timestamp=0)
# transform time series into data set
ds = ts.to_dataset(n_in=n_in, n_out=n_out)
# split the data set into train and test
# test_1 has exactly one input vector of shape (1,)
# and one output vector of shape (20,) i.e. 20 forecast steps
train, test_1 = ds.split_train_test(-1, test_n_out=steps_1)
# test_2 has 20 input vectors of shape (1,)
# and 20 out vectors of shape (1,) i.e. 1 forecast step
train_, test_2 = ds.split_train_test(-20, test_n_out=steps_2)
# prepare a very simple neural network with just one dense layer
model = NeuralNetwork()
model.add(keras.layers.Dense(units=n_out, input_shape=(n_in,)))
model.compile(optimizer=keras.optimizers.RMSprop(), loss=keras.losses.mean_squared_error)
# train the model on the train set
model.fit(train.x, train.y, epochs=500)
# forecast future values from both test input arrays
predicted_1 = model.forecast(test_1.x, steps=steps_1)
predicted_2 = model.forecast(test_2.x, steps=steps_2)Output:
expected 1:
features outputs
[179.] [180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193.
194. 195. 196. 197. 198. 199.]
predicted 1:
features outputs
[179.] [180.09366 181.1879 182.2827 183.37808 184.47404 185.57059 186.66771
187.76541 188.8637 189.96255 191.062 192.16202 193.26262 194.3638
195.46556 196.5679 197.67082 198.77432 199.8784 200.98306]
expected 2:
features outputs
[179.] [180.]
[180.] [181.]
[181.] [182.]
[182.] [183.]
[183.] [184.]
[184.] [185.]
[185.] [186.]
[186.] [187.]
[187.] [188.]
[188.] [189.]
[189.] [190.]
[190.] [191.]
[191.] [192.]
[192.] [193.]
[193.] [194.]
[194.] [195.]
[195.] [196.]
[196.] [197.]
[197.] [198.]
[198.] [199.]
predicted 2:
features outputs
[179.] [180.09366]
[180.] [181.09418]
[181.] [182.09471]
[182.] [183.09525]
[183.] [184.09576]
[184.] [185.0963]
[185.] [186.09682]
[186.] [187.09735]
[187.] [188.09787]
[188.] [189.0984]
[189.] [190.09892]
[190.] [191.09946]
[191.] [192.09998]
[192.] [193.10051]
[193.] [194.10104]
[194.] [195.10156]
[195.] [196.1021]
[196.] [197.10262]
[197.] [198.10315]
[198.] [199.10367]