Anticipy is a tool to generate forecasts for time series. It takes a pandas Series or DataFrame as input, and returns a DataFrame with the forecasted values for a given period of time.
Features:
- Simple interface. Start forecasting with a single function call on a pandas DataFrame.
- Model selection. If you provide different multiple models (e.g. linear, sigmoidal, exponential), the tool will compare them and choose the best fit for your data.
- Trend and seasonality. Support for weekly and monthly seasonality, among other types.
- Calendar events. Provide lists of special dates, such as holiday seasons or bank holidays, to improve model performance.
- Data cleaning. The library has tools to identify and remove outliers, and to detect and handle step changes in the data.
It is straightforward to generate a simple linear model with the tool - just call forecast.run_forecast(my_dataframe):
import pandas as pd, numpy as np
from anticipy import forecast
df = pd.DataFrame({'y': np.arange(0., 5)}, index=pd.date_range('2018-01-01', periods=5, freq='D'))
df_forecast = forecast.run_forecast(df, extrapolate_years=1)
print(df_forecast.head(12))Output:
. date model y is_actuals
0 2018-01-01 y 0.000000e+00 True
1 2018-01-02 y 1.000000e+00 True
2 2018-01-03 y 2.000000e+00 True
3 2018-01-04 y 3.000000e+00 True
4 2018-01-05 y 4.000000e+00 True
5 2018-01-01 linear 5.551115e-17 False
6 2018-01-02 linear 1.000000e+00 False
7 2018-01-03 linear 2.000000e+00 False
8 2018-01-04 linear 3.000000e+00 False
9 2018-01-05 linear 4.000000e+00 False
10 2018-01-06 linear 5.000000e+00 False
11 2018-01-07 linear 6.000000e+00 False
Documentation is available in Read the Docs