Siamese-like Time-series Forecasting with Prior Anomaly Detection and Inner Reconstruction

We present Exformer, a novel Siamese-like time-series forecasting Transformer with extended anomaly detection and reconstruction modules. Exformer extricates itself from addressing the non-stationarity, which is the principal bottleneck of time-series forecasting, solely from the perspective of normalization. Instead, Exformer foremost analyzes each input window with anomaly detection method before forecasting and attempts to reconstruct the anomalous parts during the forecasting period. Leveraging from this strategy, Exformer excels in mitigating the influences of pattern anomalies in input sequences, which are virtually insurmountable for existing solutions on tackling non-stationarity. To further alleviate the forecasting turbulence brought by non-stationarity, Exformer additionally employs Siamese architecture to pledge the identical feature distribution of input and forecasting windows in the latent space. Exformer is simple and direct as the anomaly detection part is devised to be non-parametric and the reconstruction part is designed to merely make modifications rather than supplementing additional components. Benefiting from exploiting these innovations, Exformer achieves state-of-the-art forecasting accuracy and robustness on eight benchmarks with considerable efficiency. The source code will be released soon.