Anticipy

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