Partial Correlation-Based Attention for Multivariate Time Series Forecasting
A multivariate time-series forecasting has great potentials in various domains. However, it is challenging to find dependency structure among the time-series variables and appropriate time-lags for each variable, which change dynamically over time. In this study, I suggest partial correlation-based attention mechanism which overcomes the shortcomings of existing pair-wise comparisons-based attention mechanisms. Moreover, I propose data-driven series-wise multi-resolution convolutional layers to represent the input time-series data for domain agnostic learning.
How to Cite
Lee, W. K. (2020). Partial Correlation-Based Attention for Multivariate Time Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13720-13721. https://doi.org/10.1609/aaai.v34i10.7132
Doctoral Consortium Track