Recently, I was asked to teach a class on forecasting using Python. I thought my notes would be a good source of information for every one interested in this area and I might learn from my reader’s feedback as well. In this part 1 of the article we will talk about basic forecasting methods — Naive, Average, Moving Average and Weighted Average. In the next parts we will discuss measures of scoring, exponential smoothing techniques, Holt, Holt winters, ARIMA, ARMA and deep learning methods using LSTM.
Incidentally Kaggle also released a competition on forecasting which plans to forecast for 145K time series. However, this article is based on M-competitions data-set. Kaggle’s competition is very similar to M3 competitions held in the past led by forecasting researcher Spyros Makridakis. The goal of the M3 competition was to be able to forecast across multiple (3003) time series. The results and learnings from the M3 competition are summarized in the paper.
In this article set I will review forecasting methods using python. The goal is to develop an intuitive understanding of forecasting for beginners. Finally, I felt there are not many mature libaries for forecasting in python, although statsmod provides…
