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When Holt-Winters is Better Than ML | Anais Dotis-Georgiou | InfluxData

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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

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When Holt-Winters is Better Than ML | Anais Dotis-Georgiou | InfluxData

  1. 1. Anais Dotis-Georgiou, DevRel, InfluxData When Holt-Winters is Better
  2. 2. © InfluxData. All rights reserved. About Me ○ Anais Jackie Dotis on LinkedIn ○ @art.anaisdg Developer Advocate, InfluxData
  3. 3. © InfluxData. All rights reserved. M3 Competition Statistical and Machine Learning forecasting methods: Concerns and ways forward” Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos
  4. 4. © InfluxData. All rights reserved. M4 Competition
  5. 5. © InfluxData. All rights reserved. Hybrid Methods Win “Slawek Smyl, a data scientist at Uber Technologies, which mixes ES formulas with a recurrent neural network (RNN) forecasting engine. Smyl clarifies that his method does not constitute a simple ensemble of exponential smoothing and neural networks .Instead, the models are truly hybrid algorithms in which all parameters, like the initial ES seasonality and smoothing coefficients, are fitted concurrently with the RNN weights by the same gradient descent method. The improvement of this method over that of Comb was close to an impressive %” -TheM Competition: , timeseriesand forecasting Methods. International Journal of Forecasting.
  6. 6. © InfluxData. All rights reserved. Naive Method where
  7. 7. © InfluxData. All rights reserved. Single Exponential Smoothing
  8. 8. © InfluxData. All rights reserved.© InfluxData. All rights reserved. Component Form
  9. 9. © InfluxData. All rights reserved.© InfluxData. All rights reserved. Linear Regression Overview of Optimization for Single Exponential Smoothing
  10. 10. © InfluxData. All rights reserved.© InfluxData. All rights reserved. Optimization of RSS for Linear Regression
  11. 11. © InfluxData. All rights reserved.
  12. 12. © InfluxData. All rights reserved.
  13. 13. © InfluxData. All rights reserved.
  14. 14. © InfluxData. All rights reserved.
  15. 15. © InfluxData. All rights reserved.
  16. 16. © InfluxData. All rights reserved.
  17. 17. © InfluxData. All rights reserved. Holt-Winters’s Multiplicative SES (For Reference)
  18. 18. © InfluxData. All rights reserved. RSS vs RMSE Numerical Method Nedler-Mead Method
  19. 19. © InfluxData. All rights reserved.© InfluxData. All rights reserved. Numerical Method Ex: LU Decomposition(An Aside)
  20. 20. © InfluxData. All rights reserved. Nedler-Mead
  21. 21. © InfluxData. All rights reserved. HOLT_WINTERS() and Influx
  22. 22. © InfluxData. All rights reserved. HOLT_WINTERS() and Influx
  23. 23. © InfluxData. All rights reserved. HOLT_WINTERS() and Influx
  24. 24. © InfluxData. All rights reserved. HOLT_WINTERS() and Influx
  25. 25. © InfluxData. All rights reserved. HOLT_WINTERS() and Influx
  26. 26. © InfluxData. All rights reserved. holtWinters() in Flux from(bucket: "NOAA_water_database") |> range(start: - y) |> filter(fn: (r) => r._field == "water_level") |> aggregateWindow(every: m, fn: first). |> holtWinters(n: , seasonality: , interval: m) Assuming no offset
  27. 27. ● ● HW Blog: https://www.influxdata.com/blog/when-you -want-holt-winters-instead-of-machine-lear ning/ ● Autocorrelation: https://www.influxdata.com/blog/autocorrel ation-in-time-series-data/ ● Pitfalls of LSTM for univariate TS: https://towardsdatascience.com/how-not-to -use-machine-learning-for-time-series-forec asting-avoiding-the-pitfalls-19f9d7adf424
  28. 28. ● anais@influxdata.com ● Community Page ○ community.influxdata.com ● Community Slack
  29. 29. Thank You!!
  30. 30. © InfluxData. All rights reserved.© InfluxData. All rights reserved. Moving Average
  31. 31. © InfluxData. All rights reserved.© InfluxData. All rights reserved. Quartile Range
  32. 32. © InfluxData. All rights reserved.© InfluxData. All rights reserved. Contextual Anomaly
  33. 33. © InfluxData. All rights reserved.© InfluxData. All rights reserved. Collective Anomaly

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