Contamination Map Methodology

Time series / Model

TimeRadar

Organisation
Singapore Management University (mala-lab)
Family
TimeRadar
Released
2026-02-22 (arXiv v1 submission date, https://arxiv.org/abs/2602.19068v1)
Weights
Unknown
Links
PaperCode

Training data disclosure: Full list

Level determined for 18% of benchmark cells.

“In this work, we leverage Monash+ as our pretraining dataset, comprising approximately 408 million time points.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15
“The abnormal time series is synthesized by injecting anomalies into the Monash normal series.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Other declared training data

Declared exclusions

Benchmark cells

GIFT-Eval

Unknown Jena Weather (GIFT-Eval) · training data not disclosed in enough detail

Unknown BizITObs - Application · training data not disclosed in enough detail

Unknown BizITObs - Service · training data not disclosed in enough detail

Unknown BizITObs - L2C · training data not disclosed in enough detail

Unknown Bitbrains - Fast Storage · training data not disclosed in enough detail

Unknown Bitbrains - rnd · training data not disclosed in enough detail

Unknown Restaurant (Recruit Restaurant Visitor Forecasting) · training data not disclosed in enough detail

Unknown ETT1 (GIFT-Eval) · training data not disclosed in enough detail

Unknown ETT2 (GIFT-Eval) · training data not disclosed in enough detail

Unknown Loop Seattle (LibCity) · training data not disclosed in enough detail

Unknown SZ-Taxi (LibCity) · training data not disclosed in enough detail

Unknown M_DENSE (LibCity) · training data not disclosed in enough detail

Unknown Solar (LSTNet, 137 Alabama PV plants) · training data not disclosed in enough detail

Unknown Hierarchical Sales (Mancuso et al., 2020) · training data not disclosed in enough detail

Unknown M4 Yearly · training data not disclosed in enough detail

Unknown M4 Quarterly · training data not disclosed in enough detail

Unknown M4 Monthly · training data not disclosed in enough detail

Unknown M4 Weekly · training data not disclosed in enough detail

Unknown M4 Daily · training data not disclosed in enough detail

Unknown M4 Hourly · training data not disclosed in enough detail

Unknown Hospital · training data not disclosed in enough detail

Unknown COVID Deaths · training data not disclosed in enough detail

Seen, split unclear US Births · the dataset was used; the source does not say which part

Evidence (2)
“Aus. E.D. Wind Wind Farms Solar Power Solar London Smart M. Saugeen River F. Sunspot Weather KDD Cup 2018 US Births FRED_MD Bitcoin NN5”
arxiv.org · paper · Appendix, Table 3 · retrieved 2026-10-15
“In this work, we leverage Monash+ as our pretraining dataset, comprising approximately 408 million time points.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Seen, split unclear Saugeen River Flow · the dataset was used; the source does not say which part

Evidence (2)
“Aus. E.D. Wind Wind Farms Solar Power Solar London Smart M. Saugeen River F. Sunspot Weather KDD Cup 2018 US Births FRED_MD Bitcoin NN5”
arxiv.org · paper · Appendix, Table 3 · retrieved 2026-10-15
“In this work, we leverage Monash+ as our pretraining dataset, comprising approximately 408 million time points.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Unknown Temperature Rain · training data not disclosed in enough detail

Seen, split unclear KDD Cup 2018 · the dataset was used; the source does not say which part

Evidence (2)
“Aus. E.D. Wind Wind Farms Solar Power Solar London Smart M. Saugeen River F. Sunspot Weather KDD Cup 2018 US Births FRED_MD Bitcoin NN5”
arxiv.org · paper · Appendix, Table 3 · retrieved 2026-10-15
“In this work, we leverage Monash+ as our pretraining dataset, comprising approximately 408 million time points.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Unknown Car Parts · training data not disclosed in enough detail

Unknown ElectricityLoadDiagrams20112014 (UCI) · training data not disclosed in enough detail

Long-horizon forecasting (ETT, Electricity, Traffic, Weather, Exchange, ILI)

Unknown ETTh1 · training data not disclosed in enough detail

Unknown ETTh2 · training data not disclosed in enough detail

Unknown ETTm1 · training data not disclosed in enough detail

Unknown ETTm2 · training data not disclosed in enough detail

Unknown Electricity (ECL, 321 clients) · training data not disclosed in enough detail

Unknown Traffic (PeMS San Francisco Bay, 862 sensors) · training data not disclosed in enough detail

Unknown Weather (Jena, 2020; Autoformer) · training data not disclosed in enough detail

Unknown Exchange-Rate · training data not disclosed in enough detail

Unknown ILI (CDC influenza-like illness, national) · training data not disclosed in enough detail

Time-series anomaly detection

Unknown TSB-AD-U (TSB-AD univariate) · training data not disclosed in enough detail

Unknown TSB-AD-M (TSB-AD multivariate) · training data not disclosed in enough detail

Not seen UCR Time Series Anomaly Archive (Hexagon ML/UCR, KDD Cup 2021) · the authors state the dataset was excluded from training

Evidence (2)
“Importantly, none of the downstream evaluation datasets are included in our pretraining data.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15
“The datasets include SMD [51], MSL [23], PSM [1], SWaT [35], SMAP [23], CICIDS [26], SWAN [26], Creditcard [26], as well as the UCR benchmark [57].”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Not seen Server Machine Dataset (SMD) · the authors state the dataset was excluded from training

Evidence (2)
“Importantly, none of the downstream evaluation datasets are included in our pretraining data.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15
“The datasets include SMD [51], MSL [23], PSM [1], SWaT [35], SMAP [23], CICIDS [26], SWAN [26], Creditcard [26], as well as the UCR benchmark [57].”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Not seen Mars Science Laboratory (MSL) telemetry anomaly dataset · the authors state the dataset was excluded from training

Evidence (2)
“Importantly, none of the downstream evaluation datasets are included in our pretraining data.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15
“The datasets include SMD [51], MSL [23], PSM [1], SWaT [35], SMAP [23], CICIDS [26], SWAN [26], Creditcard [26], as well as the UCR benchmark [57].”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Not seen Soil Moisture Active Passive (SMAP) telemetry anomaly dataset · the authors state the dataset was excluded from training

Evidence (2)
“Importantly, none of the downstream evaluation datasets are included in our pretraining data.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15
“The datasets include SMD [51], MSL [23], PSM [1], SWaT [35], SMAP [23], CICIDS [26], SWAN [26], Creditcard [26], as well as the UCR benchmark [57].”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Not seen Pooled Server Metrics (PSM) · the authors state the dataset was excluded from training

Evidence (2)
“Importantly, none of the downstream evaluation datasets are included in our pretraining data.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15
“The datasets include SMD [51], MSL [23], PSM [1], SWaT [35], SMAP [23], CICIDS [26], SWAN [26], Creditcard [26], as well as the UCR benchmark [57].”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Not seen Secure Water Treatment (SWaT) · the authors state the dataset was excluded from training

Evidence (2)
“Importantly, none of the downstream evaluation datasets are included in our pretraining data.”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15
“The datasets include SMD [51], MSL [23], PSM [1], SWaT [35], SMAP [23], CICIDS [26], SWAN [26], Creditcard [26], as well as the UCR benchmark [57].”
arxiv.org · paper · Appendix A.1 · retrieved 2026-10-15

Unknown Yahoo S5 Labeled Anomaly Detection Dataset (Yahoo Webscope S5) · training data not disclosed in enough detail

Unknown Numenta Anomaly Benchmark (NAB) · training data not disclosed in enough detail

Time-series classification

Unknown UCR Time Series Classification Archive (2018) · training data not disclosed in enough detail

Unknown UEA Multivariate Time Series Classification Archive (2018) · training data not disclosed in enough detail

Notes

Zero-shot anomaly FM pre-trained only on Monash+ (the Monash part of DADA's data, not DADA's anomaly datasets). README provides pre-training code and data links but no checkpoint; README badges show both MIT and Apache-2.0, GitHub API reports no license.

Sources