{"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/conditional-time-series-forecasting-with","title":"Conditional Time Series Forecasting with Convolutional Neural Networks","arxiv_id":"1703.04691","date":"2017-03-14","proceeding":null,"authors":["Anastasia Borovykh","Sander Bohte","Cornelis W. Oosterlee"],"abstract":"We present a method for conditional time series forecasting based on an\nadaptation of the recent deep convolutional WaveNet architecture. The proposed\nnetwork contains stacks of dilated convolutions that allow it to access a broad\nrange of history when forecasting, a ReLU activation function and conditioning\nis performed by applying multiple convolutional filters in parallel to separate\ntime series which allows for the fast processing of data and the exploitation\nof the correlation structure between the multivariate time series. We test and\nanalyze the performance of the convolutional network both unconditionally as\nwell as conditionally for financial time series forecasting using the S&P500,\nthe volatility index, the CBOE interest rate and several exchange rates and\nextensively compare it to the performance of the well-known autoregressive\nmodel and a long-short term memory network. We show that a convolutional\nnetwork is well-suited for regression-type problems and is able to effectively\nlearn dependencies in and between the series without the need for long\nhistorical time series, is a time-efficient and easy to implement alternative\nto recurrent-type networks and tends to outperform linear and recurrent models.","url_abs":"http://arxiv.org/abs/1703.04691v5","url_pdf":"http://arxiv.org/pdf/1703.04691v5.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":"conditional-time-series-forecasting-with","repo_url":"https://github.com/junwang23/deepdirtycodes","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"conditional-time-series-forecasting-with","repo_url":"https://github.com/litanli/wavenet-time-series-forecasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"conditional-time-series-forecasting-with","repo_url":"https://github.com/Abirate/Time-Series-Forecasting/blob/main/adapted-google-wavenet-time-series.ipynb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"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":[{"method_slug":"dilated-causal-convolution","method_name":"Dilated Causal Convolution"},{"method_slug":"mixture-of-logistic-distributions","method_name":"Mixture of Logistic Distributions"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"wavenet","method_name":"WaveNet"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.04691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.04691"}},"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. 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