Adaptive sliding window normalization

Abstract

Time series data, frequent in various domains such as finance, healthcare, environmental monitoring, and energy management, often exhibit nonstationary behaviors and anomalies that challenge traditional normalization techniques. This research proposes an innovative methodology termed Adaptive Sliding Window Normalization (ASWN) to address these limitations. ASWN dynamically adjusts normalization window sizes based on detected anomalies with multiple methods, applied Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for the finalization of those, and utilizes the Akaike Information Criterion (AIC) with AutoRegressive Integrated Moving Average (ARIMA) models to determine optimal window sizes in the absence of anomalies. This approach integrates multiple anomaly detection methods to ensure responsiveness to changes in data patterns and effective management of outliers. ASWN is applied to diverse time series datasets, including energy consumption, and financial data, demonstrating significant improvements in predictive accuracy. Extensive experiments show that ASWN outperforms traditional normalization methods, providing empirical evidence of its benefits in handling nonstationary and anomalous data. This research enhances the robustness and reliability of time series forecasting and contributes to the broader field by thoroughly documenting the methodology, experimental setup, and results. The findings are intended to foster further advancements in time series normalization and forecasting.