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.