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.gitattributes

@@ -57,3 +57,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+# Time-series specific
+*.ts filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
+*.xlsx filter=lfs diff=lfs merge=lfs -text

FaceDetection/FaceDetection_TEST.ts

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PEMS-SF/PEMS-SF_TEST.ts

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README.md

@@ -1,3 +1,312 @@
----
-license: mit
----
+---
+tags:
+- time-series
+- forecasting
+- anomaly-detection
+- classification
+- TSLib
+license: cc-by-4.0
+task_categories:
+- time-series-forecasting
+pretty_name: Time-Series-Library (TSLib)
+language:
+- en
+configs:
+- config_name: ETTh1
+  description: ETT long-term forecasting subset ETTh1 (hourly).
+  data_files:
+  - ETT-small/ETTh1.csv
+- config_name: ETTh2
+  description: ETT long-term forecasting subset ETTh2 (hourly).
+  data_files:
+  - ETT-small/ETTh2.csv
+- config_name: ETTm1
+  description: ETT long-term forecasting subset ETTm1 (15-min).
+  data_files:
+  - ETT-small/ETTm1.csv
+- config_name: ETTm2
+  description: ETT long-term forecasting subset ETTm2 (15-min).
+  data_files:
+  - ETT-small/ETTm2.csv
+- config_name: electricity
+  description: Electricity load forecasting (UCI Electricity).
+  data_files:
+  - electricity/electricity.csv
+- config_name: traffic
+  description: Traffic volume forecasting.
+  data_files:
+  - traffic/traffic.csv
+- config_name: weather
+  description: Weather time-series forecasting.
+  data_files:
+  - weather/weather.csv
+- config_name: exchange_rate
+  description: Exchange rate forecasting.
+  data_files:
+  - exchange_rate/exchange_rate.csv
+- config_name: national_illness
+  description: Influenza-like illness (ILI) forecasting.
+  data_files:
+  - illness/national_illness.csv
+- config_name: m4-yearly
+  description: M4 Yearly forecasting subset.
+  data_files:
+  - split: train
+    path: m4/Yearly-train.csv
+  - split: test
+    path: m4/Yearly-test.csv
+- config_name: m4-quarterly
+  description: M4 Quarterly forecasting subset.
+  data_files:
+  - split: train
+    path: m4/Quarterly-train.csv
+  - split: test
+    path: m4/Quarterly-test.csv
+- config_name: m4-monthly
+  description: M4 Monthly forecasting subset.
+  data_files:
+  - split: train
+    path: m4/Monthly-train.csv
+  - split: test
+    path: m4/Monthly-test.csv
+- config_name: m4-weekly
+  description: M4 Weekly forecasting subset.
+  data_files:
+  - split: train
+    path: m4/Weekly-train.csv
+  - split: test
+    path: m4/Weekly-test.csv
+- config_name: m4-daily
+  description: M4 Daily forecasting subset.
+  data_files:
+  - split: train
+    path: m4/Daily-train.csv
+  - split: test
+    path: m4/Daily-test.csv
+- config_name: m4-hourly
+  description: M4 Hourly forecasting subset.
+  data_files:
+  - split: train
+    path: m4/Hourly-train.csv
+  - split: test
+    path: m4/Hourly-test.csv
+- config_name: EthanolConcentration
+  description: 'UEA multivariate classification: EthanolConcentration.'
+  data_files:
+  - split: train
+    path: EthanolConcentration/EthanolConcentration_TRAIN.ts
+  - split: test
+    path: EthanolConcentration/EthanolConcentration_TEST.ts
+- config_name: FaceDetection
+  description: 'UEA multivariate classification: FaceDetection.'
+  data_files:
+  - split: train
+    path: FaceDetection/FaceDetection_TRAIN.ts
+  - split: test
+    path: FaceDetection/FaceDetection_TEST.ts
+- config_name: Handwriting
+  description: 'UEA multivariate classification: Handwriting.'
+  data_files:
+  - split: train
+    path: Handwriting/Handwriting_TRAIN.ts
+  - split: test
+    path: Handwriting/Handwriting_TEST.ts
+- config_name: Heartbeat
+  description: 'UEA multivariate classification: Heartbeat.'
+  data_files:
+  - split: train
+    path: Heartbeat/Heartbeat_TRAIN.ts
+  - split: test
+    path: Heartbeat/Heartbeat_TEST.ts
+- config_name: JapaneseVowels
+  description: 'UEA multivariate classification: JapaneseVowels.'
+  data_files:
+  - split: train
+    path: JapaneseVowels/JapaneseVowels_TRAIN.ts
+  - split: test
+    path: JapaneseVowels/JapaneseVowels_TEST.ts
+- config_name: PEMS-SF
+  description: 'UEA multivariate classification: PEMS-SF.'
+  data_files:
+  - split: train
+    path: PEMS-SF/PEMS-SF_TRAIN.ts
+  - split: test
+    path: PEMS-SF/PEMS-SF_TEST.ts
+- config_name: SelfRegulationSCP1
+  description: 'UEA multivariate classification: SelfRegulationSCP1.'
+  data_files:
+  - split: train
+    path: SelfRegulationSCP1/SelfRegulationSCP1_TRAIN.ts
+  - split: test
+    path: SelfRegulationSCP1/SelfRegulationSCP1_TEST.ts
+- config_name: SelfRegulationSCP2
+  description: 'UEA multivariate classification: SelfRegulationSCP2.'
+  data_files:
+  - split: train
+    path: SelfRegulationSCP2/SelfRegulationSCP2_TRAIN.ts
+  - split: test
+    path: SelfRegulationSCP2/SelfRegulationSCP2_TEST.ts
+- config_name: SpokenArabicDigits
+  description: 'UEA multivariate classification: SpokenArabicDigits.'
+  data_files:
+  - split: train
+    path: SpokenArabicDigits/SpokenArabicDigits_TRAIN.ts
+  - split: test
+    path: SpokenArabicDigits/SpokenArabicDigits_TEST.ts
+- config_name: UWaveGestureLibrary
+  description: 'UEA multivariate classification: UWaveGestureLibrary.'
+  data_files:
+  - split: train
+    path: UWaveGestureLibrary/UWaveGestureLibrary_TRAIN.ts
+  - split: test
+    path: UWaveGestureLibrary/UWaveGestureLibrary_TEST.ts
+- config_name: SMD-data
+  description: Server Machine Dataset (SMD) for anomaly detection — train & test data.
+  data_files:
+  - split: train
+    path: SMD/SMD_train.npy
+  - split: test
+    path: SMD/SMD_test.npy
+- config_name: SMD-label
+  description: Server Machine Dataset (SMD) — test anomaly labels.
+  data_files:
+  - split: test_label
+    path: SMD/SMD_test_label.npy
+- config_name: MSL-data
+  description: NASA Mars Science Laboratory (MSL) anomaly detection — train/test arrays.
+  data_files:
+  - split: train
+    path: MSL/MSL_train.npy
+  - split: test
+    path: MSL/MSL_test.npy
+- config_name: MSL-label
+  description: MSL anomaly detection — test labels.
+  data_files:
+  - split: test_label
+    path: MSL/MSL_test_label.npy
+- config_name: SMAP-data
+  description: >-
+    NASA Soil Moisture Active Passive (SMAP) anomaly detection — train/test
+    arrays.
+  data_files:
+  - split: train
+    path: SMAP/SMAP_train.npy
+  - split: test
+    path: SMAP/SMAP_test.npy
+- config_name: SMAP-label
+  description: SMAP anomaly detection — test labels.
+  data_files:
+  - split: test_label
+    path: SMAP/SMAP_test_label.npy
+- config_name: PSM-data
+  description: KPI-based Process/System Monitoring data (train/test).
+  data_files:
+  - split: train
+    path: PSM/train.csv
+  - split: test
+    path: PSM/test.csv
+- config_name: PSM-label
+  description: KPI-based Process/System Monitoring labels (test_label).
+  data_files:
+  - split: test_label
+    path: PSM/test_label.csv
+- config_name: SWaT
+  description: Secure Water Treatment (SWaT) anomaly detection, processed data.
+  data_files:
+  - split: train
+    path: SWaT/swat_train2.csv
+  - split: test
+    path: SWaT/swat2.csv
+size_categories:
+- 10M<n<100M
+---
+
+
+# Time-Series-Library (TSLib)
+
+TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
+
+We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: **long- and short-term forecasting, imputation, anomaly detection, and classification.**
+
+This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of current time-series models and their performance, please refer to our paper **[Deep Time Series Models: A Comprehensive Survey and Benchmark](https://arxiv.org/abs/2407.13278)**.
+
+To get started with the codebase and contribute, please visit the **[GitHub repository](https://github.com/thuml/Time-Series-Library)**.
+
+## Dataset Overview
+
+| **Tasks**         | **Benchmarks**                                                                 | **Metrics**                          | **Series Length**     |
+|-------------------|-------------------------------------------------------------------------------|--------------------------------------|-----------------------|
+| **Forecasting**    | **Long-term:** ETT (4 subsets), Electricity, Traffic, Weather, Exchange, ILI  | MSE, MAE                             | 96\~720 (ILI: 24\~60)   |
+|                   | **Short-term:** M4 (6 subsets)                                                | SMAPE, MASE, OWA                    | 6\~48                  |
+| **Imputation**     | ETT (4 subsets), Electricity, Weather                                        | MSE, MAE                             | 96                    |
+| **Classification** | UEA (10 subsets)                                                              | Accuracy                             | 29\~1751               |
+| **Anomaly Detection** | SMD, MSL, SMAP, SWaT, PSM                                                  | Precision, Recall, F1-Score         | 100                   |
+
+
+## File Structure
+```
+Time-Series-Library/
+├── ETT-small/
+├── EthanolConcentration/
+├── FaceDetection/
+├── Handwriting/
+├── Heartbeat/
+├── JapaneseVowels/
+├── MSL/
+├── PEMS-SF/
+├── PSM/
+├── SMAP/
+├── SMD/
+├── SWaT/
+├── SelfRegulationSCP1/
+├── SelfRegulationSCP2/
+├── SpokenArabicDigits/
+├── UWaveGestureLibrary/
+├── electricity/
+├── exchange_rate/
+├── illness/
+├── m4/
+├── traffic/
+├── weather/
+├── .gitattributes
+└── README.md
+```
+
+## Usage
+
+You can load the dataset directly using the `datasets` library:
+
+```
+from datasets import load_dataset
+dataset = load_dataset("thuml/Time-Series-Library", "ETTh1")
+```
+
+Or download specific files with hf_hub_download:
+
+```
+from huggingface_hub import hf_hub_download
+hf_hub_download("thuml/Time-Series-Library", "ETT-small/ETTh1.csv", repo_type="dataset")
+```
+
+## License
+This dataset is released under the CC BY 4.0 License.
+
+## Citation
+
+If you find this repo useful, please cite our paper.
+
+```
+@inproceedings{wu2023timesnet,
+  title={TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis},
+  author={Haixu Wu and Tengge Hu and Yong Liu and Hang Zhou and Jianmin Wang and Mingsheng Long},
+  booktitle={International Conference on Learning Representations},
+  year={2023},
+}
+
+@article{wang2024tssurvey,
+  title={Deep Time Series Models: A Comprehensive Survey and Benchmark},
+  author={Yuxuan Wang and Haixu Wu and Jiaxiang Dong and Yong Liu and Mingsheng Long and Jianmin Wang},
+  booktitle={arXiv preprint arXiv:2407.13278},
+  year={2024},
+}
+```

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SWaT/data.py

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+import xlrd
+import csv
+
+'''def xlsx_to_csv():
+    workbook = xlrd.open_workbook('SWaT_Dataset_Normal_v1.xlsx')
+    table = workbook.sheet_by_index(0)
+    with open('swat_train.csv', 'w', encoding='utf-8') as f:
+        write = csv.writer(f)
+        for row_num in range(table.nrows):
+            row_value = table.row_values(row_num)
+            write.writerow(row_value)
+
+if __name__ == '__main__':
+    xlsx_to_csv()'''
+'''import pandas as pd
+import csv
+f = open('swat_train2.csv','w',encoding='utf-8')
+csv_writer = csv.writer(f)
+csv_writer.writerow(['FIT101' ,'LIT101' ,' MV101', 'P101' ,'P102', ' AIT201', 'AIT202',
+ 'AIT203' ,'FIT201' ,' MV201' ,' P201', ' P202', 'P203' ,' P204', 'P205', 'P206',
+ 'DPIT301' ,'FIT301', 'LIT301' ,'MV301' ,'MV302', ' MV303', 'MV304' ,'P301'
+, 'P302', 'AIT401', 'AIT402', 'FIT401', 'LIT401', 'P401', 'P402', 'P403', 'P404',
+ 'UV401' ,'AIT501', 'AIT502', 'AIT503', 'AIT504', 'FIT501', 'FIT502' ,'FIT503',
+ 'FIT504', 'P501', 'P502', 'PIT501', 'PIT502', 'PIT503', 'FIT601', 'P601', 'P602',
+ 'P603', 'Normal/Attack'])
+
+df = pd.read_csv('swat_train.csv')
+df = df.values
+for u in range(1,len(df)):
+    tem = df[u][1:]
+    tem2 = []
+    for i in range(len(tem)-1):
+        tem2.append(float(tem[i]))
+    if(tem[-1]=='Normal'):
+        tem2.append(0)
+    else:
+        tem2.append(1)
+    csv_writer.writerow(tem2)'''
+import pandas as pd
+df = pd.read_csv('swat_train2.csv')
+df = df.values
+print(df.shape)
+ano = 0
+for u in range(len(df)):
+    if(df[u][-1]==1):
+        ano = ano+1
+print(ano)
+print(ano/len(df))
+
+

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