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