When You Want Holt-Winters Instead of Machine Learning

Machine Learning (ML) gets a lot of hype, but its Classical predecessors are still immensely powerful, especially in the time-series space. Error, Trend, Seasonality Forecast (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters are three Classical methods that are not only incredibly popular but are also excellent time-series predictors.

In fact, according to Statistical and Machine Learning forecasting methods: Concerns and ways forward, ETS outperforms several other ML methods including Long Short Term Memory (LSTM) and Recurrent Neural Networks (RNN) in One-Step Forecasting. Actually, all of the statistical methods have a lower prediction error than the ML methods do.

Bar Chart Comparing the Error for One-Step Forecasts. Taken from “Statistical and Machine Learning forecasting methods: Concerns and ways forward.”

My hope is that after finishing this three-part blog post series, you’ll have a strong conceptual and mathematical understanding of how Holt-Winters works. I focus on Holt-Winters for three reasons. First, Holt-Winters or Triple Exponential Smoothing is a sibling of ETS. If you understand Holt-Winters, then you will easily be able to understand the most powerful prediction method for time series data (among the methods above). Secondly, you can use Holt-Winters out of the box with InfluxDB. Finally, the InfluxData community has requested an explanation of Holt-Winters in this Github issue 459. Luckily for us, Holt-Winters is fairly simple…