97 – Fractal Analytics
Goal
Goal is to find the severals items demands using their historical demands. For some items, they share some common category, subcategorical info. Tricky part is not all items have simmilar historical data, some have 9 months data and while others have only few weeks of data and we have predict 60 days into future.
Issues I had
 Lack of proper testing framework or model.
 Lack of knowledge on TimeSeries Models. Example ARIMA.
 Lack of experience of time series modelling.
Learning Sources
Time Series Analysis

Econometrics – Ani Katchova: Ani, convers the most part of theoritical Knowledge to understand and have a quick start in TimeSeries Analysis. No Code – Just Theory.

Econometrics Academy: A bit abractly conveys understanding of AR, MA, ARMA and ARIMA, a bit deeper understanding. No Py Code – Just Theory.

PyData Conf – Jeffrey Yau: Theory + Hands on Code. Lots of Python Notebooks also available in Github.

PyCon 2017 – Aileen Nielsen: Complete Set. Theory + Basics + little Math + Code + Nice Notebooks in Github
Python Pkgs

PyFlux Documentation: Simple tools immediately start working with Time Series Analysis.

statsmodel – tsa (time series analysis)
Notebooks
 GitHub CookBook Resources