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Time-series forecasting serves as a linchpin in a myriad of applications,spanning various domains. With the growth of deep learning, this arena hasbifurcated into two salient branches: one focuses on crafting specific neuralarchitectures tailored for time series, and the other harnesses advanced deepgenerative models for probabilistic forecasting. While both branches have madesignificant progress, their differences across data scenarios, methodologicalfocuses, and decoding schemes pose profound, yet unexplored, researchquestions. To bridge this knowledge chasm, we introduce ProbTS, a pioneeringtoolkit developed to synergize and compare these two distinct branches. Endowedwith a unified data module, a modularized model module, and a comprehensiveevaluator module, ProbTS allows us to revisit and benchmark leading methodsfrom both branches. The scrutiny with ProbTS highlights their distinctcharacteristics, relative strengths and weaknesses, and areas that need furtherexploration. Our analyses point to new avenues for research, aiming for moreeffective time-series forecasting.

ProbTS: A Unified Toolkit to Probe Deep Time-series Forecasting