{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/temporal-pattern-attention-for-multivariate","title":"Temporal Pattern Attention for Multivariate Time Series Forecasting","arxiv_id":"1809.04206","date":"2018-09-12","proceeding":null,"authors":["Shun-Yao Shih","Fan-Keng Sun","Hung-Yi Lee"],"abstract":"Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate the task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism. Typical attention mechanism reviews the information at each previous time step and selects the relevant information to help generate the outputs, but it fails to capture the temporal patterns across multiple time steps. In this paper, we propose to use a set of filters to extract time-invariant temporal patterns, which is similar to transforming time series data into its \"frequency domain\". Then we proposed a novel attention mechanism to select relevant time series, and use its \"frequency domain\" information for forecasting. We applied the proposed model on several real-world tasks and achieved state-of-the-art performance in all of them with only one exception.","url_abs":"https://arxiv.org/abs/1809.04206v3","url_pdf":"https://arxiv.org/pdf/1809.04206v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"temporal-pattern-attention-for-multivariate","repo_url":"https://github.com/gantheory/TPA-LSTM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"temporal-pattern-attention-for-multivariate","repo_url":"https://github.com/abinashsinha330/Air-Pollution-Forecasting-using-Machine-Learning-APF-Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"temporal-pattern-attention-for-multivariate","repo_url":"https://github.com/abinashsinha330/Air-Pollution-Forecasting-using-Recurrent-Neural-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"temporal-pattern-attention-for-multivariate","repo_url":"https://github.com/abinashsinha330/Solve-Air-Air-Pollution-Forecasting-using-Deep-Attentive-Sequence-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"multivariate-time-series-forecasting","task_name":"Multivariate Time Series Forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"},{"task_slug":"univariate-time-series-forecasting","task_name":"Univariate Time Series Forecasting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/univariate-time-series-forecasting-on","task":"Univariate Time Series Forecasting","dataset":"Electricity","model":"TPA-LSTM (3 step)","rank_in_archive_order":2,"of":12,"metrics":{"RRSE":"0.0823"},"uses_additional_data":false},{"leaderboard":"/sota/univariate-time-series-forecasting-on","task":"Univariate Time Series Forecasting","dataset":"Electricity","model":"TPA-LSTM (6 step)","rank_in_archive_order":6,"of":12,"metrics":{"RRSE":"0.0916"},"uses_additional_data":false},{"leaderboard":"/sota/univariate-time-series-forecasting-on","task":"Univariate Time Series Forecasting","dataset":"Electricity","model":"TPA-LSTM (12 step)","rank_in_archive_order":9,"of":12,"metrics":{"RRSE":"0.0964"},"uses_additional_data":false},{"leaderboard":"/sota/univariate-time-series-forecasting-on","task":"Univariate Time Series Forecasting","dataset":"Electricity","model":"TPA-LSTM (24 step)","rank_in_archive_order":10,"of":12,"metrics":{"RRSE":"0.1006"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.04206","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}