Time Series Modeling Project Template for Python Version 1

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

Thanks to Dr. Jason Brownlee’s suggestions on creating a machine learning template, I have pulled together a project template that can be used to support time series analysis using the ARIMA modeling and Python.

Version 1 of the time series template is the first iteration and contains sample code segments for:

  • Visualizing the data via line plots, histogram, density plot, box plot, and whisker plot.
  • Testing for stationarity via the ACF and PACF plots
  • Testing for seasonality via the seasonal decomposition and detrending
  • Fitting models using the automated stepwise and manual grid searches
  • Evaluating models by analyzing the residuals
  • Validating the models by using the in-sample data points
  • Forecasting with the model by using the out-of-sample data points

You will find the Python time series template on the Machine Learning Project Templates page.