TIME-SERIES FORECASTING USING FEATURE BASED HYBRID APPROACH
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In this research, we explored the unique strengths of both traditional statistical method and machine learning algorithms to propose a hybrid forecasting system based on a decomposition approach, with the objective of improving forecast accuracy across multiple forecasting horizons. Our proposed methodology uses the Seasonal and Trend decomposition using Loess decomposition procedure to break down time-series data into trend-cycle, seasonal and covariance stationary components, where these components produce individual forecasts and these forecasts are aggregated back to whole using an aggregation procedure, with the sole purpose of minimizing errors. Various types of Exponential Smoothing algorithms were employed for the trend-cycle, seasonal components because of its unique weight combination approach while vectors of features were extracted automatically from the nonlinear covariance stationary subseries to create appropriate Machine Learning models.