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Robert Nau's notes on time series are a good start - https://people.duke.edu/~rnau/411home.htm https://people.duke.edu/~rnau/411home.htm Keep in mind: - not e

Robert Nau's notes on time series are a good start - https://people.duke.edu/~rnau/411home.htm https://people.duke.edu/~rnau/411home.htm Keep in mind: - not everything that can be represented as time series, should be - not everything that can be represented as time series, can be reliably forecasted - the theory on time series forecasting is dry. Really dry. - working with time series is quite different from most other ML tasks - the more advanced solutions that get mentioned in the latest research are specific to the application that was subject of the paper and often don't generalise very well

natalyarostova 8y ago
> working with time series is quite different from most other ML tasks Seconding this. I work on a time-series forecasting team. The ability of ML to work on time-series forecasting is very domain specific. If you want to predict electricity usage in the next 1-hour, and have 10 years of data, along with 10 years of data on interesting variables (e.g. weather, entertainment, whatever else), then throwing it all at a neural net or whatever you want, could be useful. Why? Because you're predicting one step ahead and have a lot of data. On the other hand, if the requirement is you produce a multi-year forecast ahead, and you don't have a lot of data to work with, this suggests you may not have a wealth of data. ML algos are great at squeezing prediction out of lots of data. Traditional statistical time-series algos are great at imposing structure through stronger distributional assumptions, which allows data to only speak to the extent that it can express itself through the structure that the algorithm allows it to speak.