# Time series forecasting with hour data, prediction for next 24 hours

i'm a newbie in Time Series Analysis. I have a 2 year pandas dataframe about water consumptions in hour granularity (24 records for day, 365 days).

                            Water_consumptions
Data
2017-01-01 00:00:00            315.546173
2017-01-01 01:00:00            322.469203
2017-01-01 02:00:00            305.497974
2017-01-01 03:00:00            291.905637
2017-01-01 04:00:00            268.990071
2017-01-01 05:00:00            267.545479
...


I would like to predict day water consumptions (the next 24 records) based on this two years. Which kind of model is most accurate for this task?

I've read about Sarimax and Recurrent Neural Network (LSTM) as a possibility. Are there other possibilities?

My series has also trending and seasonal component. Have my series to be stationarized? Why? in which way i have to use trending, seasonal and residuals in my model after my series was stationarized? i think i can't remove annual seasonality with 2 only years of data:

Plotting before stationarity:

Plotting after stationarity, except for annual seasonality:

Thanks

You very probably have : intra-daily, intra-weekly and intra-yearly. Therefore, my first choice would be models that explicitly address these. Examples of such models are and . Both are available in the forecast package for R (beware: they take a long time to fit), but I am not aware of any implementations for Python.