Multi-Resolution Sequence Aggregation and Model-Agnostic Framework for Time-Series Forecasting

In time-series forecasting, signals such as traffic volume collected in the real world are noisy and irregularly sampled due to sensor malfunctions, so it is difficult to make accurate prediction. To resolve such difficulty, downsampling can be used to reduce noise and allow capturing slow trend of signals. In addition, upsampling can fill the missing data of irregularly sampled signals to catch fine details. Although extracting multi-resolution temporal features such as down or upsampling can improve prediction accuracy, the existing time-series forecasting approaches have used the original and/or downsampled signals only, so they cannot detect fine details of upsampled one. Moreover, these methods merge multi-resolution inputs without carefully concern to chronological order of time-series, which is very important in the time-series. To overcome this challenge, we propose a framework that can fully utilize multi-resolution time-series signals in up, original, and downscale, and sequentially aggregate them, named multi-resolution sequence aggregation and model-agnostic (MAMA) framework. Note that i) MAMA aggregates the multi-resolution signals without breaking its sequential characteristics, whose effectiveness was verified by the experiment results, and ii) it can adopt any existing forecasting algorithms. From experiments with the real-world datasets, it was observed that the prediction accuracy of the well-known forecasting models (i.e., LSTNet, TCN, and Informer) were improved by 11.5% on average when the proposed architecture is used. In ablation study, we showed that a performance improvement of 1.5% was achieved with the help of sequential aggregation module.