Identifying Outliers for Climatology Time Variant Series with Sliding Window

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. For the same an algorithm, which is based on sliding window prediction is proposed in this paper. The time series are parted in accordance with the size of sliding window. Then 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 the value measured. If the difference is greater enough than the value of defined threshold then that specific point can be treated for outlier. Results from experiment are showing that the algorithm is identifying the outliers for climatology time variant series data and also remodeling the correction efficiency.