Abstract
Time Series Analysis is helpful in understanding past and current pattern of the phenomenon and provides important clue for predicting future trends. Weather forecasting is a matter of great importance in the area of Time Series Analysis. The purpose of this paper is to predict the monthly rainfall based on the historical dataset using pattern similarity based models together with k-NN technique and to compare the estimated values with the actual observations. We have applied a recently proposed approach Approximation and Prediction of Stock Time-series data (APST) for forecasting rainfall. Further we have also suggested two variations of APST. We have also compared the results of similarity based methods with Autoregressive Model. Suggested techniques yield better results than original APST and AR Models.
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Sharma, A., Bose, M. (2014). Rainfall Prediction Using k-NN Based Similarity Measure. In: Biswas, G., Mukhopadhyay, S. (eds) Recent Advances in Information Technology. Advances in Intelligent Systems and Computing, vol 266. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1856-2_14
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DOI: https://doi.org/10.1007/978-81-322-1856-2_14
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