  Multivariate time series forecasting constitutes important functionality in
cyber-physical systems, whose prediction accuracy can be improved significantly
by capturing temporal and multivariate correlations among multiple time series.
State-of-the-art deep learning methods fail to construct models for full time
series because model complexity grows exponentially with time series length.
Rather, these methods construct local temporal and multivariate correlations
within subsequences, but fail to capture correlations among subsequences, which
significantly affect their forecasting accuracy. To capture the temporal and
multivariate correlations among subsequences, we design a pattern discovery
model, that constructs correlations via diverse pattern functions. While the
traditional pattern discovery method uses shared and fixed pattern functions
that ignore the diversity across time series. We propose a novel pattern
discovery method that can automatically capture diverse and complex time series
patterns. We also propose a learnable correlation matrix, that enables the
model to capture distinct correlations among multiple time series. Extensive
experiments show that our model achieves state-of-the-art prediction accuracy.
