{"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/neural-mjd-neural-non-stationary-merton-jump","title":"Neural MJD: Neural Non-Stationary Merton Jump Diffusion for Time Series Prediction","arxiv_id":"2506.04542","date":"2025-06-05","proceeding":null,"authors":["Yuanpei Gao","Qi Yan","Yan Leng","Renjie Liao"],"abstract":"While deep learning methods have achieved strong performance in time series prediction, their black-box nature and inability to explicitly model underlying stochastic processes often limit their generalization to non-stationary data, especially in the presence of abrupt changes. In this work, we introduce Neural MJD, a neural network based non-stationary Merton jump diffusion (MJD) model. Our model explicitly formulates forecasting as a stochastic differential equation (SDE) simulation problem, combining a time-inhomogeneous It\\^o diffusion to capture non-stationary stochastic dynamics with a time-inhomogeneous compound Poisson process to model abrupt jumps. To enable tractable learning, we introduce a likelihood truncation mechanism that caps the number of jumps within small time intervals and provide a theoretical error bound for this approximation. Additionally, we propose an Euler-Maruyama with restart solver, which achieves a provably lower error bound in estimating expected states and reduced variance compared to the standard solver. Experiments on both synthetic and real-world datasets demonstrate that Neural MJD consistently outperforms state-of-the-art deep learning and statistical learning methods.","url_abs":"https://arxiv.org/abs/2506.04542v1","url_pdf":"https://arxiv.org/pdf/2506.04542v1.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":[],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2506.04542","atlas_url":"https://app.syntology.ai/?focus=2506.04542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.04542"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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":"deterministic:regex_extraction","url":"https://github.com/DSL-Lab/neural-MJD","reach":null}],"summary":{"ran":1,"ran_draft_wrong":1},"by_repo_kind":{"found_in_text":{"samples":2,"ran":2,"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":2,"samples":[{"code_sha256_prefix":"5a0ee8b71bf38c49","entry":"NeuralMJD","repo":"DSL-Lab/neural-MJD","repo_kind":"found_in_text","path":"model/mjd/neural_mjd.py","file_url":"https://github.com/DSL-Lab/neural-MJD/blob/HEAD/model/mjd/neural_mjd.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5a0ee8b71bf38c49"}},{"code_sha256_prefix":"47a5972978dceccf","entry":"mask_nodes","repo":"DSL-Lab/neural-MJD","repo_kind":"found_in_text","path":"model/mjd/neural_mjd.py","file_url":"https://github.com/DSL-Lab/neural-MJD/blob/HEAD/model/mjd/neural_mjd.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"47a5972978dceccf"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}