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Forecasting tools

Project description

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 source  is_actuals    model    y   q5  q20  q80  q95
0  2018-01-01    src        True  actuals  0.0  NaN  NaN  NaN  NaN
1  2018-01-02    src        True  actuals  1.0  NaN  NaN  NaN  NaN
2  2018-01-03    src        True  actuals  2.0  NaN  NaN  NaN  NaN
3  2018-01-04    src        True  actuals  3.0  NaN  NaN  NaN  NaN
4  2018-01-05    src        True  actuals  4.0  NaN  NaN  NaN  NaN
5  2018-01-01    src       False   linear  0.0  NaN  NaN  NaN  NaN
6  2018-01-02    src       False   linear  1.0  NaN  NaN  NaN  NaN
7  2018-01-03    src       False   linear  2.0  NaN  NaN  NaN  NaN
8  2018-01-04    src       False   linear  3.0  NaN  NaN  NaN  NaN
9  2018-01-05    src       False   linear  4.0  NaN  NaN  NaN  NaN
10 2018-01-06    src       False   linear  5.0  5.0  5.0  5.0  5.0
11 2018-01-07    src       False   linear  6.0  6.0  6.0  6.0  6.0

Documentation is available in Read the Docs

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