Forecasting Methods – Exploring Different Methods for Forecasting in Time Series Analysis

Question

Data – Monthly Rainfall of a region for the past 20 years

Objective – To Forecast for the next 2 years

I am new to time series forecasting and I am looking for suggestions on various methods that I can use in order to forecast from the above-mentioned collected data.

I have already used a SARIMA model and predictions have been done using R.

Now I need other different methods that I can use with this data for forecasting and choose the better performing model among them.

I apologize in case of the lack of clarity in the question put forth.

Answer 1

SARIMA is a good first benchmark. I would suggest three more ones, all of which use seasonality, which I would assume to be relevant for rainfall:

• Seasonal exponential smoothing. If you use forecast::ets(), it will attempt to automatically fit a "good" model with additive or multiplicative trend and/or seasonality.
• A "seasonal naive" model, where the forecast is just the last observation for the corresponding month. So the forecast for next January (and January after that) would just be last January's observation.
• A similar model, where you would use the average of previous observations in the corresponding month, e.g., using the average of all historical January observations to forecast the next January.

You may wonder why I recommend the latter two models. The fact is that in forecasting, you should always compare complicated methods to simple benchmarks, which can be surprisingly hard to beat.

As a textbook, I very much recommend Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman and Forecasting: Principles and Practice (3rd ed.) by Athanasopoulos & Hyndman.