What are suitable datasets for univariate time series forecasting with RNNs, LGBM, TBATS, SARIMA models (topic, frequency, sources)? I am currently looking for a suitable dataset (univariate time series) for short-term forecasting using lag features or moving windows of lag features to employ models like LSTM, GRU, SARIMA, LGBM, TBATS and ETS. I have been able to successfully implement the models for a hourly traffic flow dataset using no external features, but I have tried 5 different datasets since then and the simple models (ETS, ARIMA) give nonsensical point forecasts so I don't really know how to find a second suitable dataset. What can i look out for when trying to find a useful dataset if I don't want to leave it up to chance?
So far I have also explored daily Air Quality AQI data, hourly water meter consumption data and monthly electricity demand data but the point forecasts of the simpler models were never useful.
Or can it just be the case that the data were simply too random to predict?
Any advice will be much appreciated.
A dataset I used when checking the ARIMA methodology is the one you can find here about monthly air passengers history data since 1949. It shows both trend and seasonality, and it is nice to play with searching for hyperparameters (p, d, q) when implementing ARIMA.
You can find below the results I got sometime ago as a quick check (being blue the true data and red the predictions):