{"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/camul-calibrated-and-accurate-multi-view-time","title":"CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting","arxiv_id":"2109.07438","date":"2021-09-15","proceeding":null,"authors":["Harshavardhan Kamarthi","Lingkai Kong","Alexander Rodríguez","Chao Zhang","B. Aditya Prakash"],"abstract":"Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures. Leveraging information as well as uncertainty from these data sources for well-calibrated and accurate forecasts is an important challenging problem. Most previous work on multi-modal learning and forecasting simply aggregate intermediate representations from each data view by simple methods of summation or concatenation and do not explicitly model uncertainty for each data-view. We propose a general probabilistic multi-view forecasting framework CAMul, that can learn representations and uncertainty from diverse data sources. It integrates the knowledge and uncertainty from each data view in a dynamic context-specific manner assigning more importance to useful views to model a well-calibrated forecast distribution. We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\\% in accuracy and calibration.","url_abs":"https://arxiv.org/abs/2109.07438v3","url_pdf":"https://arxiv.org/pdf/2109.07438v3.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":"camul-calibrated-and-accurate-multi-view-time","repo_url":"https://github.com/adityalab/camul","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"probabilistic-time-series-forecasting","task_name":"Probabilistic 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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.07438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.07438"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/adityalab/camul","reach":null}],"summary":{"ran_honours":4,"ran_violates":1,"unverified":3},"by_repo_kind":{"official":{"samples":8,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"73da0a0e1d88544e","entry":"get_month","repo":"adityalab/camul","repo_kind":"official","path":"train_power.py","file_url":"https://github.com/adityalab/camul/blob/HEAD/train_power.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"73da0a0e1d88544e"}},{"code_sha256_prefix":"f5bd6f35e24716ef","entry":"get_time_of_day","repo":"adityalab/camul","repo_kind":"official","path":"train_power.py","file_url":"https://github.com/adityalab/camul/blob/HEAD/train_power.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f5bd6f35e24716ef"}},{"code_sha256_prefix":"0c57fdfb1eb2e872","entry":"get_week","repo":"adityalab/camul","repo_kind":"official","path":"train_tweets.py","file_url":"https://github.com/adityalab/camul/blob/HEAD/train_tweets.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0c57fdfb1eb2e872"}},{"code_sha256_prefix":"8b195a6b88d4bd87","entry":"one_hot","repo":"adityalab/camul","repo_kind":"official","path":"train_covid.py","file_url":"https://github.com/adityalab/camul/blob/HEAD/train_covid.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8b195a6b88d4bd87"}},{"code_sha256_prefix":"9994d524f6d845c5","entry":"week_to_month","repo":"adityalab/camul","repo_kind":"official","path":"train_tweets.py","file_url":"https://github.com/adityalab/camul/blob/HEAD/train_tweets.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9994d524f6d845c5"}},{"code_sha256_prefix":"e9030d5fa7cc8d5b","entry":"CorrEncoder","repo":"adityalab/camul","repo_kind":"official","path":"models/multimodels.py","file_url":"https://github.com/adityalab/camul/blob/HEAD/models/multimodels.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e9030d5fa7cc8d5b"}},{"code_sha256_prefix":"9d913046ed9f7007","entry":"FFN","repo":"adityalab/camul","repo_kind":"official","path":"models/multimodels.py","file_url":"https://github.com/adityalab/camul/blob/HEAD/models/multimodels.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9d913046ed9f7007"}},{"code_sha256_prefix":"1ac2773c38fa886e","entry":"sample_bipartite","repo":"adityalab/camul","repo_kind":"official","path":"models/multimodels.py","file_url":"https://github.com/adityalab/camul/blob/HEAD/models/multimodels.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1ac2773c38fa886e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}