# Model to forecast data

I would know what are the best predictive model to forecast consumption data with R or Python, I have got data from one month (each day and each 15 minutes) and i have to forecast data for tomorrow for each 15 minutes (00h00, 00h15,...,23h45).

Thank You

Edit :

X1 : Names of region

X2 : Date (01/01/2017,...,Today)

X3 : Hour (00h00, 00h15, 00h30,...,23h45)

X4 : Power consumption (5000, 6000,...,5500)

X5 : Production of Power - 1 (400,200,...)

X6 : Production of Power - 2 (10000,12000,...)

X7 : Production of Power - 3 (400,200,...)

X8 : Production of Power - 4 (100,200,...)

X9 : Production of Power - 5 (0,2,...)

X10 : Production of Power - 6 (4000,2000,...)

X11 : Production of Power - 7 (0,-2000,-10,...)

X12 : Difference between consumption and production (-8000,-8000,...)

I have to predict X4, that's my TARGET, I heard about ARIMA, time series is it the best model in this case ?

• what is your data and what do you want to forecast ? state your variables. – Subhash C. Davar May 21 '17 at 22:52
• I edited my post – John_Trump May 21 '17 at 23:32

Warm welcome to data mining!
You will need to do some serious learning.
The best model for a given problem can't be identified theoretically. It's more a trial and error exercise based on experience. You will also need a lot of data with as much as possible helper research as possible. Like energy consumption may be influenced by:

• Day is a weekday or a weekend
• Or maybe better: day of week
• Length of the night vs. length of daylight
• Industrial activity in the area
• ...make some research for more ideas

This is feature engineering or 'hacking', take a look into it.

Since you are planning to forecast something the choices are a dense DNN or a multi layer RNN. A dense DNN is more simple but it can be used for prediction only as a benchmark. Any proper RNN models must be superior to a dense DNN.
Secondly you need more data. Forecasting a day based on a month of data may be too ambitious.

I would like to suggest that you go in the direction with learning:

• Data normalization
• Deep neural networks (Dense)
• Recurring neural networks (RNN), like LSTM or GRU. Ultimately you will need these.

I started with these sources, but you may prefer other points to start:
Artificial Intelligence for Humans

Application of deep neural networks

P.S: I'm not sure that X5 through X12 adds any value for the prediction. I believe that would need more industry specific knowledge from me.