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I have a time-series data containing 16 values (of no. of Vehicles) from 2001 to 2016. I wanted to predict - based on the underlying trend - the values upto 2050 (which is a long shot I agree).

Upon doing some research, I found that it can be done by methods like HoltWinters or TBATS which, even though, did not go with my own plan of using some Machine Learning algorithm.

I am using R for all my work. Now, after using HoltWinters() and then forecast() methods, I did get an extrapolated curve uptil 2050 but it is a simple exponential curve from 2017 to 2050 which I think I could have obtained through meager calculations.

My question is twofold:

1) What would be the best approach to obtain a meaningful extrapolation?

2) Does my current approach be modified to give me a more meaningful extrapolation?

By meaningful I want to express that a curve with the details more closer to actuality.

Thanks a lot.

Abdul Basit Khan
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1 Answers1

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I guess you need more data to make predictions. HoltWinters or TBATS may work but there are many other ML models for time series data you can try.

http://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html

This link has the R sample code for Holtwinters and the plots.

My3
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