> 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

> 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.