When Holt-Winters is better than machine learning
Who is this presentation for?
- Data scientists, developers, and analysts
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 also excellent time series predictors. In fact, these classical methods outperform several other ML methods including long short-term memory (LTSM) and recurrent neural networks (RNNs) in one-step forecasting.
Anais Dotis dives into how the Holt-Winters forecasting algorithm works. Then you’ll use the HOLT_WINTERS() function with InfluxData to make your own time series forecast.
- Experience with calculus
What you'll learn
- Understand when to use Holt-Winters, how single exponential smoothing works, the optimization for single exponential smoothing, how single exponential smoothing relates to triple exponential smoothing and Holt-Winters, and how you can use InfluxDB’s built-in multiplicative Holt-Winters function to generate predictions on your time series data
Anais Dotis-Georgiou is a developer advocate at InfluxData with a passion for making data beautiful using data analytics, AI, and machine learning. She takes the data that she collects and does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she’s not behind a screen, you can find her outside drawing, stretching, or chasing after a soccer ball.
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