A Denoising Time Window Algorithm for Optimizing LSTM Prediction

Realistic anomalous data noise is usually locally distributed in nature. To address the challenge posed by clustered anomalous noise in time series data, which not only reduces the accuracy of forecasting models but also presents a unique challenge due to its clustered nature and existing methods’ limitations in handling such noise, this study introduces a novel denoising algorithm leveraging an optimized Long Short-Term Memory (LSTM) network, named ‘Denoising Time Window Optimized LSTM’ (DTW-LSTM), which innovatively identifies and rectifies clustered anomalous noise in time series data, significantly enhancing prediction accuracy by adeptly balancing the denoising process and data integrity preservation, as evidenced by comprehensive experimental validations. The algorithm employs a sliding time window mechanism to analyze the original series and its Fourier approximation, identifying clustered anomalies through a clustering model. It then uses a symmetrical data mixing and crossover technique to enhance the data quality in noisy segments. This process preserves the integrity of time series data by maintaining a balance between the original and fitted series, leading to a significant improvement in prediction accuracy. Experimental results indicate that the algorithm can adeptly discern the presence of clustered anomalous noise within the current time window and effectively rectify sections of data containing such noise. By surpassing the performance benchmarks set by contemporary forecasting models, this algorithm has established itself as the new SOTA method in the field of time series forecasting. Each sub-model contributes to the superior accuracy and reliability of the algorithm.