Change Point Detection for Automatic Time Series Forecasting

Time series analysis, the process of learning patterns in time series data and making future predictions, is a challenging machine learning endeavor: the intricate and error-prone process of manual data fitting limits efficiency and scalability to larger datasets. Our research builds upon structure discovery research, paving the way for more effective short-term forecasting. We combine the adaptability of automatic change point detection with a fixed kernel composition, achieving accuracy comparable to traditional manual methods while reducing analysis time. This hybrid approach offers the best of both worlds: leveraging human expertise for precise fitting and capturing specific trends while utilizing an automated technique to recognize shifts and model complex relationships within the data dynamically. By demonstrating the effectiveness of automatic change point detection in conjunction with kernel composition, we work to develop time series usage in data analysis.