{"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/adarnn-adaptive-learning-and-forecasting-of","title":"AdaRNN: Adaptive Learning and Forecasting of Time Series","arxiv_id":"2108.04443","date":"2021-08-10","proceeding":null,"authors":["Yuntao Du","Jindong Wang","Wenjie Feng","Sinno Pan","Tao Qin","Renjun Xu","Chongjun Wang"],"abstract":"Time series has wide applications in the real world and is known to be difficult to forecast. Since its statistical properties change over time, its distribution also changes temporally, which will cause severe distribution shift problem to existing methods. However, it remains unexplored to model the time series in the distribution perspective. In this paper, we term this as Temporal Covariate Shift (TCS). This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data. AdaRNN is sequentially composed of two novel algorithms. First, we propose Temporal Distribution Characterization to better characterize the distribution information in the TS. Second, we propose Temporal Distribution Matching to reduce the distribution mismatch in TS to learn the adaptive TS model. AdaRNN is a general framework with flexible distribution distances integrated. Experiments on human activity recognition, air quality prediction, and financial analysis show that AdaRNN outperforms the latest methods by a classification accuracy of 2.6% and significantly reduces the RMSE by 9.0%. We also show that the temporal distribution matching algorithm can be extended in Transformer structure to boost its performance.","url_abs":"https://arxiv.org/abs/2108.04443v2","url_pdf":"https://arxiv.org/pdf/2108.04443v2.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":"adarnn-adaptive-learning-and-forecasting-of","repo_url":"https://github.com/jindongwang/transferlearning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"adarnn-adaptive-learning-and-forecasting-of","repo_url":"https://github.com/microsoft/qlib/tree/main/examples/benchmarks/ADARNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"financial-analysis","task_name":"Financial Analysis"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adarnn","method_name":"AdaRNN"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"ts","method_name":"TS"},{"method_slug":"temporal-distribution-characterization","method_name":"Temporal Distribution Characterization"},{"method_slug":"temporal-distribution-matching","method_name":"Temporal Distribution Matching"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[{"slug":"adarnn","name":"AdaRNN","full_name":"AdaRNN"},{"slug":"temporal-distribution-characterization","name":"Temporal Distribution Characterization","full_name":"Temporal Distribution Characterization"},{"slug":"temporal-distribution-matching","name":"Temporal Distribution Matching","full_name":"Temporal Distribution Matching"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.04443","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}