Weather Prediction through Sliding Window Algorithm and Deep Learning

Shubham Billus¹ , Shivam Billus² , Rishab Behl³

1. University of Petroleum and Energy Studies, Dehradun, Uttrakhand, India.
2. University of Petroleum and Energy Studies, Dehradun, Uttrakhand, India.
3. University of Petroleum and Energy Studies, Dehradun, Uttrakhand, India.

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.5 , pp.20-24, Oct-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i5.2024

Online published on Oct 31, 2018

Copyright © Shubham Billus, Shivam Billus, Rishab Behl . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
There are currently many techniques present in the world to predict weather. However, none of them is sufficient in itself to accurately predict the weather on any given day, 100% of the time. At best what we have yet achieved is to come up with ways to reduce errors in current systems to make weather prediction more accurate. We, humans, have used satellite sensors to predict weather, we have used pattern recognition algorithms to predict future patterns in weather, but none is as accurate as we would like them to be. One such method to predict weather through pattern recognition is Sliding Window technique. This technique predicts weather through the data available to the system about previous year’s weather around that time. However, this technique is far from being even usable when it comes to efficiency, although it is a very fast method to predict weather and involve very less computations as compared to other techniques. Only if we can find a way to make this algorithm more efficient, that we can take advantage of its fast computational speeds to actually benefit from it. This paper presents a solution to greatly improve the efficiency of this method.

Key-Words / Index Term :
Weather forecast, Sliding Window Algorithm, Deep Learning

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