Best algorithm for time series prediction?

I have to predict on a daily basis the total water demand in a certain area, creating a model based on 4 CVSs files containing:

• water demand in aggregated form (time series with daily granularity, 2 years data)
• amount of water entering the area's cistern (time series with daily granularity, 2 years data)
• amount of water leaving the area's cistern (time series with daily granularity, 2 years data)
• water request from 4,000 measurements points across the area (time series with daily granularity, 2 years data).

In your opinion, what is the best model for having a good prediction of the water demand in the area, using the available data and features? I can only think of LSTMs or MLP, I don't know if something like ARIMA or (SARIMA) could be useful in this case, seeing that I have many features but not many days.

Forecasting is inevitably a domain-specific problem because you can often make better decisions about model and methods when you know something about the system or process you are trying to forecast.

There are quite a few academic papers on forecasting domestic water demand which you could look at if you have access:

E.g.

• Demand Forecasting for Water Distribution Systems by Chen and Boccelli (2014)
• Urban Water Demand Forecasting: Review of Methods and Models by Donkor et al (2014)
• Predicting water demand: a review of the methods employed and future possibilities by de Souza Groppo et. al (2019)

I'm not an expert in this domain so you should probably wait for someone who is to answer the question but I think using an auto-regressive model (e.g. ARIMA), as you have suggested, is a good start because demand is essentially due to aggregate human activity which is inherently driven by daily / weekly routines, and seasonal effects.

There are various routines to fit such models to data. Jason Brownlee has a nice tutorial here using Python's statsmodels.tsa package.

You could also see what people have used for residential energy consumption forecasting as the problem is probably very similar to water demand forecasting.