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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 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 <https://anticipy.readthedocs.io/en/latest/>`_