I set up a forecasting model that predicts call data. The forecast model uses a random forest regression model.
Data: I have call data about every day in 15 minutes intervals of a year since 2013.
Here is a plot of the accumulated values over months:
It can be clearly seen that call data has almost doubled in 2017 over 2016. This trend should also be observable for the next few years.
First, the format of my data:
DATE CALL .... 2017-10-23 10:15:00.000 259 2017-10-23 10:30:00.000 292 2017-10-23 10:45:00.000 309 ....
From this I extracted the following features: I have extracted the following features to predict my target variable Y (call data):
-Weekday -Month -Holiday (yes / no) -Interval of the day So I ask my model: What is the call volume of a day and interval with the following features?
I have used the years 2015-2016-2017 to train the model. However, the model does not give the desired prognosis.
He even predicts the days for 2017 wrong. Although I gave him the data as training data.
- Should I work on my features? - How do I show my forecasting model that the data will double year by year as observable since 2016?