When Holt-Winters is better than Machine Learning
Who is this presentation for?Data Scientist, Developer, Analyst, Beginner
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 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 (RNN) in One-Step Forecasting. In this talk, I’ll show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series forecast.
What you'll learn
Anais Jackie Dotis
Anais Dotis-Georgiou is a Developer Advocate for InfluxData with a passion for making data beautiful with the use of Data Analytics, AI, and Machine Learning. She takes the data that she collects, does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she is not behind a screen, you can find her outside drawing, stretching, or chasing after a soccer ball.
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