•Restaurants take longer than expected to make food •Items get missed - we have to go back and get them •Drivers become unavailable (flat tyre etc) •Customers hard to find It gets harder …
Statsmodels supports this out of the box Forecasting Daily Volume # Decompose the raw time series decomposition = sm.tsa.seasonal_decompose(data.values, freq=7) # Extract individual components all_trend = decomposition.trend all_seasonal = decomposition.seasonal all_resid = decomposition.resid
1. While not as powerful as R, Statsmodels does give you core time series tools 2. Seasonal decomposition is very meaningful to human beings 3. By using all python, we were able to ship quickly Stuff we learned