  Modern time series forecasting methods, such as Transformer and its variants,
have shown strong ability in sequential data modeling. To achieve high
performance, they usually rely on redundant or unexplainable structures to
model complex relations between variables and tune the parameters with
large-scale data. Many real-world data mining tasks, however, lack sufficient
variables for relation reasoning, and therefore these methods may not properly
handle such forecasting problems. With insufficient data, time series appear to
be affected by many exogenous variables, and thus, the modeling becomes
unstable and unpredictable. To tackle this critical issue, in this paper, we
develop a novel algorithmic framework for inferring the intrinsic latent
factors implied by the observable time series. The inferred factors are used to
form multiple independent and predictable signal components that enable not
only sparse relation reasoning for long-term efficiency but also reconstructing
the future temporal data for accurate prediction. To achieve this, we introduce
three characteristics, i.e., predictability, sufficiency, and identifiability,
and model these characteristics via the powerful deep latent dynamics models to
infer the predictable signal components. Empirical results on multiple real
datasets show the efficiency of our method for different kinds of time series
forecasting. The statistical analysis validates the predictability of the
learned latent factors.
