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: