When You Want Holt-Winters Instead of Machine Learning

Anais Dotis-Georgiou explains that classical machine learning methods such as Error, Trend, Seasonality Forecast (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters are powerful time-series predictors that outperform many other ML methods including Long Short Term Memory (LSTM) and Recurrent Neural Networks (RNN) in One-Step Forecasts. The author focuses on Holt-Winters for three reasons: it's a sibling of ETS, can be used out of the box with InfluxDB, and the InfluxData community has requested an explanation of Holt-Winters. The article is divided into three parts, covering when to use Holt-Winters, how Single Exponential Smoothing works, and how to optimize its parameters using optimization techniques such as minimizing the Residual Sum of Squared Errors (RSS).

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.