This paper’s purpose is to enhance multivariate time series forecasting accuracy by introducing a novel preprocessing step: leveraging advanced image deep learning denoising models via a transfer learning approach. We specifically evaluate denoising convolutional neural network (DnCNN), deep residual neural network (ResDNN) and deep residual network (DRNet) on “individual household electric power consumption” and “the Rainforest Automation Energy” (RAE) datasets. The study aims to demonstrate that denoising significantly improves predictive accuracy and computational efficiency. Our findings highlight ResDNN as the superior denoiser, reducing RMSE by 20–30% and training time by up to 40%. This research underscores the potential of advanced denoising to refine real-world time series data for more accurate and efficient forecasting.
Our methodology integrates advanced image deep learning denoising models as a crucial preprocessing step for multivariate time series forecasting. We apply three state-of-the-art denoisers – DnCNN, ResDNN and DRNet – to raw time series data from “individual household electric power consumption” and “the RAE” datasets. The denoised time series are then fed into a subsequent forecasting model (e.g. long short-term memory (LSTM) and gated recurrent unit, etc.; though not explicitly named in the abstract, it is implied by “forecasting model”) to evaluate the enhancement. Through comparative analysis using root mean square error (RMSE) and training time, we assess each denoiser’s efficacy in improving predictive accuracy and computational efficiency compared to a baseline without denoising. This transfer learning approach aims to refine input data quality, leading to superior forecasting performance.
Our findings demonstrate the significant benefits of integrating advanced deep learning denoising as a preprocessing step for multivariate time series forecasting. Among the evaluated models, ResDNN consistently emerged as the top performer across “individual household electric power consumption” and “the RAE” datasets. Specifically, ResDNN achieved a notable reduction in RMSE values, ranging from 20% to 30% compared to forecasts made without any denoising. Furthermore, ResDNN also exhibited substantial improvements in computational efficiency, leading to reductions in training time of up to 40%. These results underscore the promise of such methodologies for enhancing real-world time series forecasting.
This study introduces a novel approach to multivariate time series forecasting by pioneering the use of pretrained image deep learning denoisers as a preprocessing step. To our knowledge, this is the first time that models like DnCNN, ResDNN and DRNet, originally developed for image denoising, have been systematically applied and evaluated in this specific context. We leverage their inherent ability to extract clean signals from noisy data, traditionally in visual domains and transfer this capability to time series. This unique methodological integration offers a fresh perspective on enhancing data quality for forecasting models, moving beyond conventional statistical denoising methods and opening new avenues for cross-domain application of deep learning advancements.
