Outlier Detection in Climatology Time Series with Sliding Window Prediction
Manish Mahajan1, Santosh Kumar2, Bhasker Pant3
1Manish Mahajan, Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, India.
2Santosh Kumar, Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, India.
3Bhasker Pant, Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, India..
Manuscript received on 06 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 966-970 | Volume-8 Issue-10, August 2019 | Retrieval Number: J91230881019/2019©BEIESP | DOI: 10.35940/ijitee.J9123.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: It is important to identify outliers for climatology series data. With better quality of data decision capability will improve which in turn will improve the complete operation. An algorithm utilising the sliding window prediction method is being proposed to improve the data decision capability in this paper. The time series are parted in accordance with the size of sliding window. Thereafter a prediction model is rooted with the help of historical data to forecast the new values. There is a pre decided threshold value which will be compared to the difference of predicted and measured value. If the difference is greater than a predefined threshold then the specific point will be treated as an outlier. Results from experiment are showing that the algorithm is identifying the outliers in climatology time series data and also remodeling the correction efficiency.
Keywords: Climatology data, forecast model, Outliers, sliding window, time series.
Scope of the Article: Regression and Prediction