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

1. Anais Dotis-Georgiou, DevRel, InfluxData
When Holt-Winters is Better

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About Me
○ Anais Jackie Dotis on LinkedIn
○ @art.anaisdg
Developer Advocate, InfluxData

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M3 Competition
Statistical and Machine Learning forecasting
methods: Concerns and ways forward”
Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos

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M4 Competition

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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.

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Naive Method
where

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Single Exponential Smoothing

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Component Form

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Linear Regression
Overview of Optimization for Single Exponential
Smoothing

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Optimization of RSS for Linear Regression

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Holt-Winters’s Multiplicative
SES (For Reference)

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RSS vs RMSE
Numerical Method
Nedler-Mead Method

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Numerical Method Ex: LU Decomposition(An
Aside)

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Nedler-Mead

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HOLT_WINTERS() and Influx

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HOLT_WINTERS() and Influx

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HOLT_WINTERS() and Influx

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HOLT_WINTERS() and Influx

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HOLT_WINTERS() and Influx

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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. ●
● 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

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● Community Page
○ community.influxdata.com
● Community Slack

29. Thank You!!

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Moving Average

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Quartile Range

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Contextual Anomaly

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Collective Anomaly