I have an ensemble of 231 time series, the largest among them being 14 lines long. The task at hand is to try to predict these time-series. But I'm finding this difficult due to the very small size of the data. Any suggestions about what algorithm to use? I'm thinking about going for a hidden markov model, but I don't know if that's a wise choice.
Of course depends on your type of data, but Holt-Winter models can have different degree of complexity and use moving average, trend, and seasonality. This is most useful if the data is not hierarchical, meaning that the time-series are independent from each other. If time-series are relatives of each other then you can also try aggregating them, predict at aggregate level and then disaggregate. The following can be a good resource: