  Delivering precise point and distributional forecasts across a spectrum of
prediction horizons represents a significant and enduring challenge in the
application of time-series forecasting within various industries. Prior
research on developing deep learning models for time-series forecasting has
often concentrated on isolated aspects, such as long-term point forecasting or
short-term probabilistic estimations. This narrow focus may result in skewed
methodological choices and hinder the adaptability of these models to uncharted
scenarios. While there is a rising trend in developing universal forecasting
models, a thorough understanding of their advantages and drawbacks, especially
regarding essential forecasting needs like point and distributional forecasts
across short and long horizons, is still lacking. In this paper, we present
ProbTS, a benchmark tool designed as a unified platform to evaluate these
fundamental forecasting needs and to conduct a rigorous comparative analysis of
numerous cutting-edge studies from recent years. We dissect the distinctive
data characteristics arising from disparate forecasting requirements and
elucidate how these characteristics can skew methodological preferences in
typical research trajectories, which often fail to fully accommodate essential
forecasting needs. Building on this, we examine the latest models for universal
time-series forecasting and discover that our analyses of methodological
strengths and weaknesses are also applicable to these universal models.
Finally, we outline the limitations inherent in current research and underscore
several avenues for future exploration.
