Papers › Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from...

Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators

31 Jan 2024arXiv:2401.17548archive 2025-07-28

Lifan Zhao, Yanyan Shen

Recently, channel-independent methods have achieved state-of-the-art performance in multivariate time series (MTS) forecasting. Despite reducing overfitting risks, these methods miss potential opportunities in utilizing channel dependence for accurate predictions. We argue that there exist locally stationary lead-lag relationships between variates, i.e., some lagged variates may follow the leading indicators within a short time period. Exploiting such channel dependence is beneficial since leading indicators offer advance information that can be used to reduce the forecasting difficulty of the lagged variates. In this paper, we propose a new method named LIFT that first efficiently estimates leading indicators and their leading steps at each time step and then judiciously allows the lagged variates to utilize the advance information from leading indicators. LIFT plays as a plugin that can be seamlessly collaborated with arbitrary time series forecasting methods. Extensive experiments on six real-world datasets demonstrate that LIFT improves the state-of-the-art methods by 5.5% in average forecasting performance. Our code is available at https://github.com/SJTU-Quant/LIFT.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2401.17548")

Code

Syntology Ran 8 of 11 code samples harvested from 2 repositories linked to this paper; 3 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 1 ran · our draft was wrong; 5 ran with no contract checked.

By repository: official repository: 11 samples from 2 repositories, 8 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

sjtu-dmtai/lift officialmentioned in papermentioned on GitHubpytorch report
sjtu-quant/lift officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

11 samples harvested; 8 ran; 1 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
1ran · violated contract
1ran · our draft was wrong
5ran
3unverified

Licence: 11 of the 11 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: 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. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

ComplexLinear sjtu-quant/lift/models/LIFT.py official repository ran · metamorphic tier: well formed no licence file found · pointer only · de8100ea138b1a4d · report
PositionalEncoding SJTU-DMTai/LIFT/layers/PatchTST_layers.py official repository ran · honoured contract no licence file found · pointer only · 16b9b78d429636b7 · report
cross_corr_coef sjtu-quant/lift/models/LIFT.py official repository ran fingerprinted no licence file found · pointer only · e7a794c3d5786636 · report
estimate_indicator sjtu-quant/lift/models/LIFT.py official repository ran fingerprinted no licence file found · pointer only · c8f9f310e7e40636 · report
get_activation_fn SJTU-DMTai/LIFT/layers/PatchTST_layers.py official repository ran no licence file found · pointer only · 72b9272542f279c6 · report
instance_norm sjtu-dmtai/lift/models/LIFT.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · b89d71d4cfdaa92d · report
shifted_leader_seq sjtu-quant/lift/models/LIFT.py official repository ran no licence file found · pointer only · abd5ad5f9040ea25 · report
str_to_bool sjtu-dmtai/lift/run_longExp.py official repository ran · violated contract no licence file found · pointer only · ece7c69991e1b54f · report
Coord2dPosEncoding SJTU-DMTai/LIFT/layers/PatchTST_layers.py official repository unverified no licence file found · pointer only · c3fc6992d1e467f3 · report
FilterFactory sjtu-quant/lift/models/LIFT.py official repository unverified no licence file found · pointer only · 0fe09ccead991c9d · report
LeadRefiner sjtu-quant/lift/models/LIFT.py official repository unverified no licence file found · pointer only · a12e5331bf2308b1 · report

Tasks

Multivariate Time Series ForecastingTime SeriesTime Series Forecasting

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Time Series Forecasting ETTh1 (336) Multivariate PatchTST + LIFT MAE 0.435 #31 of 72 Archive leaderboard report
Time Series Forecasting ETTh1 (336) Multivariate PatchTST + LIFT MSE 0.433 #31 of 72 Archive leaderboard report
Time Series Forecasting ETTh1 (336) Multivariate DLinear + LIFT MAE 0.453 #39 of 72 Archive leaderboard report
Time Series Forecasting ETTh1 (336) Multivariate DLinear + LIFT MSE 0.453 #39 of 72 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections