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time_features_from_frequency_str

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

time_features_from_frequency_str appears in the code Syntology harvested for 20 papers, as 3 distinct code bodies found in 20 places (a place is one code body under one paper). At least one of them ran in 0 of the papers; 0 of the code bodies carry 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 time_features_from_frequency_str 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 0 of the 3 distinct code bodies named time_features_from_frequency_str; 3 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
0ran · fixture could not drive it
0ran
3unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 6 of the 20 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

20 papers shown of 20, newest first; 20 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 1 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
Abstain Mask Retain Core: Time Series Prediction by Adaptive Masking Loss with Representation Consistency added by Syntology 2025-10 (from id) Secilia-Cxy/SOFTS/utils/timefeatures.py f8544563682146e5 unverified MIT (permissive)
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative 13 Feb 2025 idea-isail-lab-uiuc/tats/utils/timefeatures.py f8544563682146e5 unverified MIT (permissive)
Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation 9 Jan 2025 kimanki/tafas/tta/tafas.py 7870a2c691d73fab unverified licence not identified · pointer only
FilterNet: Harnessing Frequency Filters for Time Series Forecasting 3 Nov 2024 wanghq21/MICN/utils/timefeatures.py f8544563682146e5 unverified no licence file found · pointer only
BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting 3 Oct 2024 xiaolin-cs/BackTime/timefeatures.py f8544563682146e5 unverified no licence file found · pointer only
P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks 5 Jun 2024 NeuroCompLab-psu/PSpikeSSMs/src/dataloaders/et.py f8544563682146e5 unverified MIT (permissive)
Are Self-Attentions Effective for Time Series Forecasting? 27 May 2024 dongbeank/CATS/utils/timefeatures.py f8544563682146e5 unverified MIT (permissive)
TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting 14 Mar 2024 atik-ahamed/timemachine/TimeMachine_supervised/utils/timefeatures.py f8544563682146e5 unverified Apache-2.0 (permissive)
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling 5 Mar 2024 jimmylihui/genbench/src/dataloaders/et.py f8544563682146e5 unverified Apache-2.0 (permissive)
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis 31 Oct 2023 nzl5116190/Basisformer/data_provider/timefeatures.py f8544563682146e5 unverified no licence file found · pointer only
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting 15 Oct 2023 liuxu77/unitime/utils/timefeatures.py f8544563682146e5 unverified Apache-2.0 (permissive)
Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors 4 Oct 2023 idoamos/not-from-scratch/src/dataloaders/et.py f8544563682146e5 unverified MIT (permissive)
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction 18 Aug 2023 koa-fin/dva/utils/timefeatures.py f8544563682146e5 unverified AGPL-3.0 (copyleft) · pointer only
LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters 16 Aug 2023 blacksnail789521/LLM4TS/utils/timefeatures.py f8544563682146e5 unverified MIT (permissive)
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors 30 May 2023 thuml/Koopa/utils/timefeatures.py f8544563682146e5 unverified MIT (permissive)
Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping 18 May 2023 identical code first harvested elsewhere f8544563682146e5 unverified licence of this copy not recorded
Simplified State Space Layers for Sequence Modeling 9 Aug 2022 leonty1/deepldnn/src/dataloaders/et.py f8544563682146e5 unverified Apache-2.0 (permissive)
TiSAT: Time Series Anomaly Transformer 10 Mar 2022 kevaldoshi17/TiSAT/utils/timefeatures.py f8544563682146e5 unverified MIT (permissive)
arXiv:ijcai2025_1178 D2I-Group/awesome-vision-time-series/src/time2img/utils/timefeatures.py f8544563682146e5 unverified MIT (permissive)
arXiv:ijcai2025_0747 ChujieXu/CDPNet/utils/timefeatures.py 842c4a4846b120fd 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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