Keywords
Modified
Decomposition
Forecasting
Smoothing
Abstract
Understanding time series components has been challenging in forecasting over a long time. Time series component identification plays a critical role in understanding underlying structures such as trend, seasonality, and irregular variations in temporal data. This study provides a comprehensive evaluation of the Modified Prophet Model (MPM) with a specific focus on its capability for automated component identification. The research examines the theoretical framework of Prophet as well as the Modified Prophet Model, its additive modeling structure, and its handling of trend changepoints and multiple seasonalities. Through a structured methodological approach, the study highlights the strengths and limitations of Prophet in accurately identifying time series components across different data contexts. The findings established an improved prophet in terms of performance through technical automation and enhanced models for robust forecasting and analysis.
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