Unveiling the Pinnacle of Time Series Forecasting: Replicating and Comparing State-of-the-Art Models

Time sequences constitute a distinctive category of data, captured in uniform time intervals across extended temporal spans. The endeavor of time sequence prediction revolves around foreseeing these progressions into subsequent periods. This undertaking bears considerable significance within commercial and ecological contexts, as precise predictions equip us to ready ourselves for the envisaged scenarios, thus enabling us to fully capitalize on them or enact appropriate mitigation strategies. A plethora of methodologies have been postulated for the purpose of time sequence forecasting. While initial iterations encompassed straightforward linear auto-regressive models, the field has since witnessed an influx of diverse methodologies, including matrix factorization, transformers, deep, recurrent, convolutional, and graph neural networks. We replicate outcomes from various research papers, undertake comparative analyses of distinct models using identical datasets, and conclusively verify that the prevailing cutting-edge model indeed excels in the realm of predicting time sequences.