I have performed Boxcox transformation on my time series data and processed it through ARIMA modeling. Converted prediction values to the actual. I see significant differences between actual and predictions(In sample), would like to get optimal prediction values which are close to actual values.

Have tried Machine learning models such as Linear Regression, tree-based(Random Forest, XGboost) as well. Before processing data through these models, it(sales) was aggregated by day wise and extracted, sale day, month, sale dow..etc.

For the same data, lagged features and rolling statistics were also generated, trained and tested. However, did not get optimal sale predictions and found differences between actual sale values and predictions.

I have ensure no white noise in my data and made stationary before processing data thru models.

Can someone suggest techniques such as feature engineering or any other modeling to overcome the problem of differences between actual and predictions and future forecasting?

Sample data

Actual   Predcition
72      310.255
2148    1246.419
208     497.629
531     257.541
80      125.156
480     464.411
229     365.513
60      296.102
192     279.578
90      278.131
72      180.8
70      48.544
16      341.464
32      225.55
1560    253.01
960     708.296
1528    498.59
  • $\begingroup$ Well, a lot of problems would be solved if there was an easy way to get rid of the difference between predicted and true value. Unfortunately we don't live in a perfect world. $\endgroup$
    – Erwan
    Jul 25 '19 at 10:59
  • $\begingroup$ @Erwan, oh oh, there is no way to solve this problem. All trying to do is predict and forecast the salesperson's daily sale volume of a product. Am wondering how this kind of problems are solved in the real world. $\endgroup$
    – Optimizor
    Jul 26 '19 at 10:13
  • $\begingroup$ It's difficult to answer, it's a very broad question and you don't give a lot of information about the data. Maybe the performance you obtain is close to the best that can be obtained with this data, or maybe not: it all depends how much the actual value depends on chance or on variables which are not in the data. For example maybe the weather affects sales volumes, who knows. $\endgroup$
    – Erwan
    Jul 26 '19 at 11:35

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