Having previously worked with ARIMA and other AR-related models, this is a very comprehensive introduction of ARIMA which anyone interested in learning more about time-series forecasting can benefit from.
One point of critique though which is not immediately mentioned in the article: Depending on your data, and especially if your granularity is measured in anything under minutes, you might find the need to continuously calibrate your parameters as to ensure performance over time. I found myself running optimisations in parallel when deploying an ARIMA-inspired model for my use case, for instance. If continuously computing optimisations is not an option, it might be worthwhile implementing outlier detection or simple thresholds to prompt optimisation calls when needed. In either case, ARIMA is definitely a fun rabbit hole to explore if you have not previously worked with time-series forecasting!
One point of critique though which is not immediately mentioned in the article: Depending on your data, and especially if your granularity is measured in anything under minutes, you might find the need to continuously calibrate your parameters as to ensure performance over time. I found myself running optimisations in parallel when deploying an ARIMA-inspired model for my use case, for instance. If continuously computing optimisations is not an option, it might be worthwhile implementing outlier detection or simple thresholds to prompt optimisation calls when needed. In either case, ARIMA is definitely a fun rabbit hole to explore if you have not previously worked with time-series forecasting!