MIEL: Enhancing Long- and Ultra-Long-Term Time-Series Forecasting With Multiscale Input and Ensemble Linear Networks

Deep learning methodologies have shown impressive effectiveness in tackling time series tasks; nevertheless, a significant issue revolves around the inherent trade-off between the length of the look-back window (data input length) and prediction accuracy. In general, expanding the look-back window tends to positively influence predictive tasks. However, the fixed nature of the look-back window restricts both the accuracy and temporal scope of predictive models. Consequently, this paper introduces a novel approach named the Multi-scale Input Ensemble Linear Network (MIEL), aimed at leveraging historical time series data comprehensively while addressing the complexities of ultra-long time series prediction. MIEL generates multi-scale time series inputs through random forward sampling, thus extending the predictive capabilities of the model for extended time series. Simultaneously, the model integration strategy, which uses the same parameters to process data at different scales, reduces the number of parameters and memory usage when handling multi-scale data. Empirical results demonstrate that MIEL outperforms the eight most recent methodologies in terms of prediction accuracy especially for ultra-long time series prediction, while only marginally increasing memory utilization. The code has been released to github1