@article{Lee_2020, title={Partial Correlation-Based Attention for Multivariate Time Series Forecasting}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/7132}, DOI={10.1609/aaai.v34i10.7132}, abstractNote={<p>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.</p>}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lee, Won Kyung}, year={2020}, month={Apr.}, pages={13720-13721} }