Forecast Model recognize future trend 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.

features:

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.

Questions:

- 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?
Before going into modelling, I guess you can do a bit more of exploratory analysis(month by month, year by year). If you find any trend or seasonality and so on.

Why did you go to RF directly without using basing Techniques like ARIMA, ARMA, Exponential Smoothening and AR so on.

Sometime RF might not give you as good results as Base models, I think you don't have trend, this is from your graph(but not certain). If you can try doing some research and see if there are some external factors which are effecting your demand. Why did that happen and what is the root case for it.

To your model to understand, it needs some feature which explains its spikes somehow, it can achieved by doing feature engg/ research