Encoding Temporal Statistical-space Priors via Augmented Representation
CoRR(2024)
摘要
Modeling time series data remains a pervasive issue as the temporal dimension
is inherent to numerous domains. Despite significant strides in time series
forecasting, high noise-to-signal ratio, non-normality, non-stationarity, and
lack of data continue challenging practitioners. In response, we leverage a
simple representation augmentation technique to overcome these challenges. Our
augmented representation acts as a statistical-space prior encoded at each time
step. In response, we name our method Statistical-space Augmented
Representation (SSAR). The underlying high-dimensional data-generating process
inspires our representation augmentation. We rigorously examine the empirical
generalization performance on two data sets with two downstream temporal
learning algorithms. Our approach significantly beats all five up-to-date
baselines. Moreover, the highly modular nature of our approach can easily be
applied to various settings. Lastly, fully-fledged theoretical perspectives are
available throughout the writing for a clear and rigorous understanding.
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