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I've got a time series with seasonal and trend components, the interval is 1 hour (the size of the input data is: 24 values per a day * 365 days), the task is to predict 24 values (for the next day). What I've tried so far (with the help of stats.stackexchange, thanks!):

  • Linear regression with multiple features (is_weekday, hour etc)
  • FB Prophet
  • ARIMA (SARIMAX)
  • LSTM neural network

The problem is that though FB Prophet and SARIMAX perform the best, I can't integrate them in my Java code (I've found a few libraries on GitHub, but they generate some random forecast in practice).

I probably can hardcode the weights of my LSTM model into my Java code as well as use linear regression (there's a simple library for that).

Are there any other techniques to forecast a time series with seasonal and trend components I've missed?

Thanks.

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