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FFT_for_Period

Syntologyentry name in harvested coderead from the graph 2026-09-24

FFT_for_Period appears in the code Syntology harvested for 11 papers, as 4 distinct code bodies found in 11 places (a place is one code body under one paper). At least one of them ran in 4 of the papers; 1 of the code bodies carries a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named FFT_for_Period do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 2 of the 4 distinct code bodies named FFT_for_Period; 2 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
1ran · fixture could not drive it
1ran
2unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 7 of the 11 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “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, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

11 papers shown of 11, newest first; 11 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 2 papers added by Syntology; 2 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's 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.

PaperDateFileStatus SyntologyLicence
Extreme Adaptive Transformer for Time Series Forecasting EXTREME ADAPTIVE TRANSFORMER FOR TIME SERIES FORECASTING added by Syntology 2026-07 (from id) sanzexstha/Exformer/models/my_method/build_model_util.py 15503d40fcb7d6f1 ran Apache-2.0 (permissive)
TIMEGUARD: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting added by Syntology 2026-05 (from id) qducnguyen/TimeGuard/forecast_models/TimesNet.py e5424df493dc7897 unverified no licence file found · pointer only
arXiv:2506.03964 2025-06 (from id) kimanki/CAROTS/models/carots/modeling_carots.py 968923b047f89239 ran · fixture could not drive it fingerprinted licence not identified · pointer only
Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis 7 Nov 2024 WuQiangXDU/Peri-midFormer/model/PerimidFormer.py 5a9238c04a8fe7ad unverified no licence file found · pointer only
BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting 3 Oct 2024 xiaolin-cs/BackTime/forecast_models/TimesNet.py e5424df493dc7897 unverified no licence file found · pointer only
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective 27 Sep 2024 ForestsKing/GLAFF/backbone/TimesNet/layer.py 968923b047f89239 ran · fixture could not drive it fingerprinted no licence file found · pointer only
CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting 8 Jun 2024 zclzcl0223/CMamba/models/TimesNet.py e5424df493dc7897 unverified MIT (permissive)
From Similarity to Superiority: Channel Clustering for Time Series Forecasting 31 Mar 2024 graph-and-geometric-learning/timeseriesccm/models/timesnet.py e5424df493dc7897 unverified no licence file found · pointer only
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting 31 Dec 2023 yozhibo/msgnet/models/MSGNet.py e5424df493dc7897 unverified no licence file found · pointer only
Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting 23 Aug 2023 GRYGY1215/MTPNet/models/build_model_util.py 15503d40fcb7d6f1 ran Apache-2.0 (permissive)
arXiv:ijcai2024_0629 zhouziyu02/SDformer/models/TimesNet.py e5424df493dc7897 unverified MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the 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 cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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