A Novel Hybrid Approach for Time Series Forecasting: Period Estimation and Climate Data Analysis Using Unsupervised Learning and Spline Interpolation

Abstract

This article explores a novel approach to time series forecasting applied to the context of Chennai's climate data. Our methodology comprises two distinct established time series models, leveraging their strengths in handling seasonality and periods. Notably, a new algorithm is developed to compute the period of the time series using unsupervised machine learning and spline interpolation techniques. Through a meticulous ensembling process that combines these two models, we achieve optimized forecasts. This research contributes to advancing forecasting techniques and offers valuable insights into climate data analysis.