Developer Advocate, InfluxData
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, boarding or chasing after a soccer ball.
Talk Title: When Holt-Winters is better than ML
ML gets a lot of hype, but its statistical 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.