Time series Algorithm suggestion I have a univariate timeseries data which is always cumulative. So the trend is always upwards. I want to build timeseries forecast for future forecast. Currently i have 5 different models running: Auto-Arima, Holtswinter, Holtsmethod, FBprophet and LSTM. I want to replace LSTM with a simple timeseries model. Can you suggest me some? I tried searching and tried XGboost and random forest, but output was giving flat forecast

As you have a time series which is non-stationary, your most important step will be to stationarise the time series as much as possible. It's difficult to recommend precisely without seeing your data but potentially:

*

*Difference the data to make it non-cumulative.

*Subtract mean and divide by standard deviation.

Given a more appropriate time series target (and that your problem is univariate), I think the most simple methods would be any autoregressive-based method (e.g. ARIMA).
