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Datasets:
smilegeng
/
fev_datasets

Tasks:
Time Series Forecasting
Modalities:
Time-series
ArXiv:
Tags:
timeseries
time-series
tsfile
forecasting
License:
Dataset card Files
xet
 Community
smilegeng commited on
Commit
17df3d8
·
0 Parent(s):

Squash history to drop stale LFS blobs (re-encoded TsFiles only)

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This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. .gitattributes +242 -0
  2. ETT/15T/15T.tsfile +3 -0
  3. ETT/1D/1D.tsfile +0 -0
  4. ETT/1H/1H.tsfile +3 -0
  5. ETT/1W/1W.tsfile +0 -0
  6. ETT/README.md +68 -0
  7. LOOP_SEATTLE/1D/1D.tsfile +3 -0
  8. LOOP_SEATTLE/1H/1H_1.tsfile +3 -0
  9. LOOP_SEATTLE/1H/1H_2.tsfile +3 -0
  10. LOOP_SEATTLE/1H/1H_3.tsfile +3 -0
  11. LOOP_SEATTLE/5T/5T_1.tsfile +3 -0
  12. LOOP_SEATTLE/5T/5T_10.tsfile +3 -0
  13. LOOP_SEATTLE/5T/5T_11.tsfile +3 -0
  14. LOOP_SEATTLE/5T/5T_12.tsfile +3 -0
  15. LOOP_SEATTLE/5T/5T_13.tsfile +3 -0
  16. LOOP_SEATTLE/5T/5T_14.tsfile +3 -0
  17. LOOP_SEATTLE/5T/5T_15.tsfile +3 -0
  18. LOOP_SEATTLE/5T/5T_16.tsfile +3 -0
  19. LOOP_SEATTLE/5T/5T_17.tsfile +3 -0
  20. LOOP_SEATTLE/5T/5T_18.tsfile +3 -0
  21. LOOP_SEATTLE/5T/5T_19.tsfile +3 -0
  22. LOOP_SEATTLE/5T/5T_2.tsfile +3 -0
  23. LOOP_SEATTLE/5T/5T_20.tsfile +3 -0
  24. LOOP_SEATTLE/5T/5T_21.tsfile +3 -0
  25. LOOP_SEATTLE/5T/5T_22.tsfile +3 -0
  26. LOOP_SEATTLE/5T/5T_23.tsfile +3 -0
  27. LOOP_SEATTLE/5T/5T_24.tsfile +3 -0
  28. LOOP_SEATTLE/5T/5T_25.tsfile +3 -0
  29. LOOP_SEATTLE/5T/5T_26.tsfile +3 -0
  30. LOOP_SEATTLE/5T/5T_27.tsfile +3 -0
  31. LOOP_SEATTLE/5T/5T_28.tsfile +3 -0
  32. LOOP_SEATTLE/5T/5T_29.tsfile +3 -0
  33. LOOP_SEATTLE/5T/5T_3.tsfile +3 -0
  34. LOOP_SEATTLE/5T/5T_30.tsfile +3 -0
  35. LOOP_SEATTLE/5T/5T_31.tsfile +3 -0
  36. LOOP_SEATTLE/5T/5T_32.tsfile +3 -0
  37. LOOP_SEATTLE/5T/5T_33.tsfile +3 -0
  38. LOOP_SEATTLE/5T/5T_4.tsfile +3 -0
  39. LOOP_SEATTLE/5T/5T_5.tsfile +3 -0
  40. LOOP_SEATTLE/5T/5T_6.tsfile +3 -0
  41. LOOP_SEATTLE/5T/5T_7.tsfile +3 -0
  42. LOOP_SEATTLE/5T/5T_8.tsfile +3 -0
  43. LOOP_SEATTLE/5T/5T_9.tsfile +3 -0
  44. LOOP_SEATTLE/README.md +63 -0
  45. M_DENSE/1D/1D.tsfile +0 -0
  46. M_DENSE/1H/1H.tsfile +3 -0
  47. M_DENSE/README.md +62 -0
  48. README.md +103 -0
  49. SZ_TAXI/15T/15T.tsfile +3 -0
  50. SZ_TAXI/1H/1H.tsfile +3 -0
.gitattributes ADDED
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.avro filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.lz4 filter=lfs diff=lfs merge=lfs -text
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+ *.mds filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ # Audio files - compressed
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+ *.wav filter=lfs diff=lfs merge=lfs -text
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+ # Image files - uncompressed
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+ *.png filter=lfs diff=lfs merge=lfs -text
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+ *.tiff filter=lfs diff=lfs merge=lfs -text
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+ # Image files - compressed
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+ *.jpg filter=lfs diff=lfs merge=lfs -text
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+ *.jpeg filter=lfs diff=lfs merge=lfs -text
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+ *.webp filter=lfs diff=lfs merge=lfs -text
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+ # Video files - compressed
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
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+ *.webm filter=lfs diff=lfs merge=lfs -text
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+ epf_be/epf_be.tsfile filter=lfs diff=lfs merge=lfs -text
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+ ETT/15T/15T.tsfile filter=lfs diff=lfs merge=lfs -text
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+ ETT/1H/1H.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/1D/1D.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/1H/1H_1.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/1H/1H_2.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/1H/1H_3.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_1.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_10.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_11.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_12.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_13.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_14.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_15.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_16.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_17.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_18.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_19.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_2.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_20.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_21.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_22.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_23.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_24.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_25.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_26.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_27.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_28.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_29.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_3.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_30.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_31.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_32.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_6.tsfile filter=lfs diff=lfs merge=lfs -text
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+ LOOP_SEATTLE/5T/5T_7.tsfile filter=lfs diff=lfs merge=lfs -text
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+ SZ_TAXI/15T/15T.tsfile filter=lfs diff=lfs merge=lfs -text
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+ SZ_TAXI/1H/1H.tsfile filter=lfs diff=lfs merge=lfs -text
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131
+ favorita_stores/1D/1D_2.tsfile filter=lfs diff=lfs merge=lfs -text
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+ favorita_stores/1W/1W.tsfile filter=lfs diff=lfs merge=lfs -text
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+ favorita_transactions/1D/1D.tsfile filter=lfs diff=lfs merge=lfs -text
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+ favorita_transactions/1W/1W.tsfile filter=lfs diff=lfs merge=lfs -text
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147
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214
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232
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236
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237
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+ walmart/walmart.tsfile filter=lfs diff=lfs merge=lfs -text
242
+ world_life_expectancy/world_life_expectancy.tsfile filter=lfs diff=lfs merge=lfs -text
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Binary file (7.29 kB). View file
 
ETT/README.md ADDED
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1
+ ---
2
+ license: other
3
+ task_categories:
4
+ - time-series-forecasting
5
+ tags:
6
+ - time-series
7
+ - tsfile
8
+ pretty_name: ETT (TsFile format)
9
+ ---
10
+
11
+ # ETT — TsFile 格式
12
+
13
+ 本目录是 [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets) 中 **ETT** 子集转换为 [Apache TsFile](https://tsfile.apache.org/) 格式的版本。
14
+
15
+ ## 来源与引用
16
+
17
+ - **原始来源**：https://github.com/zhouhaoyi/ETDataset
18
+ - **论文/引用**：[[1]](https://arxiv.org/abs/2012.07436)
19
+ - **统一格式合集**：[autogluon/fev_datasets](https://huggingface.co/datasets/autogluon/fev_datasets)
20
+
21
+ > 本数据由外部来源转换为统一格式后再转为 TsFile。许可与引用以**原始来源**为准，我们不对原始数据主张任何权利。除非另有说明，数据仅供研究用途。
22
+
23
+ ## 数据统计
24
+
25
+ | 频率 | 序列数 | 中位长度 | 观测点数 | 动态列 | 静态列 | 文件 |
26
+ |---|---|---|---|---|---|---|
27
+ | 15T | 2 | 69,680 | 975,520 | 7 | 0 | `15T/15T.tsfile` |
28
+ | 1D | 2 | 724 | 10,136 | 7 | 0 | `1D/1D.tsfile` |
29
+ | 1H | 2 | 17,420 | 243,880 | 7 | 0 | `1H/1H.tsfile` |
30
+ | 1W | 2 | 103 | 1,442 | 7 | 0 | `1W/1W.tsfile` |
31
+
32
+ ## TsFile 存储模型
33
+
34
+ - 每条原始序列（`id`）→ 一个 **device**（TAG 维度）。
35
+ - 随时间变化的 target / 动态协变量 → **measurement**（FIELD）。
36
+ - `timestamp` → `Time`（INT64 毫秒）。
37
+ - 表名：ETT_15T, ETT_1D, ETT_1H, ETT_1W。
38
+
39
+ ### 列含义
40
+
41
+ | 列 | 角色 | TsFile 类型 |
42
+ |---|---|---|
43
+ | `Time` | Time（时间列） | INT64 |
44
+ | `id` | TAG（device 维度） | STRING |
45
+ | `HUFL` | FIELD（measurement） | FLOAT |
46
+ | `HULL` | FIELD（measurement） | FLOAT |
47
+ | `MUFL` | FIELD（measurement） | FLOAT |
48
+ | `MULL` | FIELD（measurement） | FLOAT |
49
+ | `LUFL` | FIELD（measurement） | FLOAT |
50
+ | `LULL` | FIELD（measurement） | FLOAT |
51
+ | `OT` | FIELD（measurement） | FLOAT |
52
+
53
+ ## 转换说明
54
+
55
+ - 每行原始数据是一整条序列 `(id, timestamp[], 各 target[])`，纵向打平为长表后写入 TsFile。
56
+ - 数值类型按源列自适应：float32→FLOAT、float64→DOUBLE、整数→INT64、bool→BOOLEAN。
57
+ - 时间精度：毫秒（INT64）。
58
+ - 大表会被工具自动分片为 `<名>_1.tsfile`、`<名>_2.tsfile` …，同属一个逻辑表。
59
+
60
+ ## 读取示例
61
+
62
+ ```python
63
+ from tsfile import TsFileReader
64
+
65
+ reader = TsFileReader("15T/15T.tsfile")
66
+ schemas = reader.get_all_table_schemas()
67
+ # 表名：ETT_15T；列见下方"列含义"。
68
+ ```
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1
+ ---
2
+ license: other
3
+ task_categories:
4
+ - time-series-forecasting
5
+ tags:
6
+ - time-series
7
+ - tsfile
8
+ pretty_name: LOOP_SEATTLE (TsFile format)
9
+ ---
10
+
11
+ # LOOP_SEATTLE — TsFile 格式
12
+
13
+ 本目录是 [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets) 中 **LOOP_SEATTLE** 子集转换为 [Apache TsFile](https://tsfile.apache.org/) 格式的版本。
14
+
15
+ ## 来源与引用
16
+
17
+ - **原始来源**：https://huggingface.co/datasets/Salesforce/GiftEval
18
+ - **论文/引用**：[[2]](https://arxiv.org/abs/2304.14343)
19
+ - **统一格式合集**：[autogluon/fev_datasets](https://huggingface.co/datasets/autogluon/fev_datasets)
20
+
21
+ > 本数据由外部来源转换为统一格式后再转为 TsFile。许可与引用以**原始来源**为准，我们不对原始数据主张任何权利。除非另有说明，数据仅供研究用途。
22
+
23
+ ## 数据统计
24
+
25
+ | 频率 | 序列数 | 中位长度 | 观测点数 | 动态列 | 静态列 | 文件 |
26
+ |---|---|---|---|---|---|---|
27
+ | 1D | 323 | 365 | 117,895 | 1 | 0 | `1D/1D.tsfile` |
28
+ | 1H | 323 | 8,760 | 2,829,480 | 1 | 0 | `1H/1H_1..1H_3.tsfile`（3 片） |
29
+ | 5T | 323 | 105,120 | 33,953,760 | 1 | 0 | `5T/5T_1..5T_33.tsfile`（33 片） |
30
+
31
+ ## TsFile 存储模型
32
+
33
+ - 每条原始序列（`id`）→ 一个 **device**（TAG 维度）。
34
+ - 随时间变化的 target / 动态协变量 → **measurement**（FIELD）。
35
+ - `timestamp` → `Time`（INT64 毫秒）。
36
+ - 表名：LOOP_SEATTLE_1D, LOOP_SEATTLE_1H, LOOP_SEATTLE_5T。
37
+
38
+ ### 列含义
39
+
40
+ | 列 | 角色 | TsFile 类型 |
41
+ |---|---|---|
42
+ | `Time` | Time（时间列） | INT64 |
43
+ | `id` | TAG（device 维度） | STRING |
44
+ | `target` | FIELD（measurement） | FLOAT |
45
+
46
+ > 注：有 969 个原始 id 含非法标识符字符，已规范化为合法 device 名（如 0→_0, 1→_1, 2→_2）。
47
+
48
+ ## 转换说明
49
+
50
+ - 每行原始数据是一整条序列 `(id, timestamp[], 各 target[])`，纵向打平为长表后写入 TsFile。
51
+ - 数值类型按源列自适应：float32→FLOAT、float64→DOUBLE、整数→INT64、bool→BOOLEAN。
52
+ - 时间精度：毫秒（INT64）。
53
+ - 大表会被工具自动分片为 `<名>_1.tsfile`、`<名>_2.tsfile` …，同属一个逻辑表。
54
+
55
+ ## 读取示例
56
+
57
+ ```python
58
+ from tsfile import TsFileReader
59
+
60
+ reader = TsFileReader("1D/1D.tsfile")
61
+ schemas = reader.get_all_table_schemas()
62
+ # 表名：LOOP_SEATTLE_1D；列见下方"列含义"。
63
+ ```
M_DENSE/1D/1D.tsfile ADDED
Binary file (83 kB). View file
 
M_DENSE/1H/1H.tsfile ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d6919b11de4115a6622ed823618637730c655cca4e08dc3607557eaa2acfef5e
3
+ size 1603020
M_DENSE/README.md ADDED
@@ -0,0 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: other
3
+ task_categories:
4
+ - time-series-forecasting
5
+ tags:
6
+ - time-series
7
+ - tsfile
8
+ pretty_name: M_DENSE (TsFile format)
9
+ ---
10
+
11
+ # M_DENSE — TsFile 格式
12
+
13
+ 本目录是 [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets) 中 **M_DENSE** 子集转换为 [Apache TsFile](https://tsfile.apache.org/) 格式的版本。
14
+
15
+ ## 来源与引用
16
+
17
+ - **原始来源**：https://huggingface.co/datasets/Salesforce/GiftEval
18
+ - **论文/引用**：[[2]](https://arxiv.org/abs/2304.14343)
19
+ - **统一格式合集**：[autogluon/fev_datasets](https://huggingface.co/datasets/autogluon/fev_datasets)
20
+
21
+ > 本数据由外部来源转换为统一格式后再转为 TsFile。许可与引用以**原始来源**为准，我们不对原始数据主张任何权利。除非另有说明，数据仅供研究用途。
22
+
23
+ ## 数据统计
24
+
25
+ | 频率 | 序列数 | 中位长度 | 观测点数 | 动态列 | 静态列 | 文件 |
26
+ |---|---|---|---|---|---|---|
27
+ | 1D | 30 | 730 | 21,900 | 1 | 0 | `1D/1D.tsfile` |
28
+ | 1H | 30 | 17,520 | 525,600 | 1 | 0 | `1H/1H.tsfile` |
29
+
30
+ ## TsFile 存储模型
31
+
32
+ - 每条原始序列（`id`）→ 一个 **device**（TAG 维度）。
33
+ - 随时间变化的 target / 动态协变量 → **measurement**（FIELD）。
34
+ - `timestamp` → `Time`（INT64 毫秒）。
35
+ - 表名：M_DENSE_1D, M_DENSE_1H。
36
+
37
+ ### 列含义
38
+
39
+ | 列 | 角色 | TsFile 类型 |
40
+ |---|---|---|
41
+ | `Time` | Time（时间列） | INT64 |
42
+ | `id` | TAG（device 维度） | STRING |
43
+ | `target` | FIELD（measurement） | FLOAT |
44
+
45
+ > 注：有 60 个原始 id 含非法标识符字符，已规范化为合法 device 名（如 0→_0, 1→_1, 2→_2）。
46
+
47
+ ## 转换说明
48
+
49
+ - 每行原始数据是一整条序列 `(id, timestamp[], 各 target[])`，纵向打平为长表后写入 TsFile。
50
+ - 数值类型按源列自适应：float32→FLOAT、float64→DOUBLE、整数→INT64、bool→BOOLEAN。
51
+ - 时间精度：毫秒（INT64）。
52
+ - 大表会被工具自动分片为 `<名>_1.tsfile`、`<名>_2.tsfile` …，同属一个逻辑表。
53
+
54
+ ## 读取示例
55
+
56
+ ```python
57
+ from tsfile import TsFileReader
58
+
59
+ reader = TsFileReader("1D/1D.tsfile")
60
+ schemas = reader.get_all_table_schemas()
61
+ # 表名：M_DENSE_1D；列见下方"列含义"。
62
+ ```
README.md ADDED
@@ -0,0 +1,103 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: other
3
+ task_categories:
4
+ - time-series-forecasting
5
+ tags:
6
+ - time-series
7
+ - tsfile
8
+ - forecasting
9
+ pretty_name: FEV datasets (TsFile format)
10
+ ---
11
+
12
+ # FEV 预测数据集合集 — TsFile 格式
13
+
14
+ 本仓库是 [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets) 转换为 [Apache TsFile](https://tsfile.apache.org/) 格式的版本，共 **49 个子集**。每个子集一个目录，含 `.tsfile` 数据文件（大表自动分片为多个 `.tsfile`）与说明 `README.md`。
15
+
16
+ > 本数据由外部来源转换为统一格式后再转为 TsFile。许可与引用以**原始来源**为准，我们不对原始数据主张任何权利。除非另有说明，数据仅供研究用途。
17
+
18
+ ## 转换说明
19
+
20
+ - `id`（每条序列）→ TsFile **device**（TAG 维度）。
21
+ - 静态协变量列 → 也作 **TAG**（device 元数据）。
22
+ - target / 动态协变量 → **measurement**（FIELD）。
23
+ - `timestamp` → `Time`（INT64 毫秒）；dtype 按源自适应（float32→FLOAT 等）。
24
+ - 路径与原仓一致：`<子集>/<频率>/<频率>.tsfile`（无频率为 `<子集>/<子集>.tsfile`）。
25
+
26
+ ## 子集索引
27
+
28
+ | 子集 | 频率 | 序列数 | 观测点数 | 来源 | 引用 |
29
+ |---|---|---|---|---|---|
30
+ | [ETT](./ETT/README.md) | 15T, 1D, 1H, 1W | 2 | 975,520 / 10,136 / 243,880 / 1,442 | [link](https://github.com/zhouhaoyi/ETDataset) | [[1]](https://arxiv.org/abs/2012.07436) |
31
+ | [LOOP_SEATTLE](./LOOP_SEATTLE/README.md) | 1D, 1H, 5T | 323 | 117,895 / 2,829,480 / 33,953,760 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[2]](https://arxiv.org/abs/2304.14343) |
32
+ | [M_DENSE](./M_DENSE/README.md) | 1D, 1H | 30 | 21,900 / 525,600 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[2]](https://arxiv.org/abs/2304.14343) |
33
+ | [SZ_TAXI](./SZ_TAXI/README.md) | 15T, 1H | 156 | 464,256 / 116,064 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[2]](https://arxiv.org/abs/2304.14343) |
34
+ | [australian_tourism](./australian_tourism/README.md) | — | 89 | 3,204 | [link](https://robjhyndman.com/publications/hierarchical-tourism/) | [[3]](https://doi.org/10.1016/j.ijforecast.2008.07.004) |
35
+ | [bizitobs_l2c](./bizitobs_l2c/README.md) | 1H, 5T | 1 | 18,648 / 223,776 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
36
+ | [boomlet](./boomlet/README.md) | 1062, 1209, 1225, 1230, 1282, 1487, 1631, 1676, 1855, 1975, 2187, 285, 619, 772, 963 | 1 | 344,064 / 868,352 / 802,816 / 376,832 / 573,440 / 884,736 / 418,520 / 1,046,300 / 272,012 / 392,325 / 523,100 / 1,228,800 / 851,968 / 1,097,728 / 458,752 | [link](https://huggingface.co/datasets/Datadog/BOOM) | [[5]](https://arxiv.org/abs/2505.14766) |
37
+ | [ecdc_ili](./ecdc_ili/README.md) | — | 25 | 4,797 | [link](https://github.com/EU-ECDC/Respiratory_viruses_weekly_data/blob/main/data/snapshots/2025-08-08_ILIARIRates.csv) | — |
38
+ | [entsoe](./entsoe/README.md) | 15T, 1H, 30T | 6 | 6,310,512 / 1,577,592 / 3,155,220 | [link](https://data.open-power-system-data.org/time_series/2020-10-06) | [[6]](https://doi.org/10.25832/time_series/2020-10-06) |
39
+ | [epf_be](./epf_be/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
40
+ | [epf_de](./epf_de/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
41
+ | [epf_fr](./epf_fr/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
42
+ | [epf_np](./epf_np/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
43
+ | [epf_pjm](./epf_pjm/README.md) | — | 1 | 157,248 | [link](https://zenodo.org/records/4624805) | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
44
+ | [ercot](./ercot/README.md) | 1D, 1H, 1M, 1W | 8 | 51,616 / 1,238,976 / 1,688 / 7,368 | [link](https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy) | — |
45
+ | [favorita_stores](./favorita_stores/README.md) | 1D, 1M, 1W | 1,579 | 10,661,408 / 255,798 / 1,136,880 | [link](https://www.kaggle.com/competitions/store-sales-time-series-forecasting) | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
46
+ | [favorita_transactions](./favorita_transactions/README.md) | 1D, 1M, 1W | 51 | 258,264 / 5,508 / 24,480 | [link](https://www.kaggle.com/competitions/store-sales-time-series-forecasting) | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
47
+ | [fred_md_2025](./fred_md_2025/README.md) | — | 1 | 100,548 | [link](https://www.stlouisfed.org/research/economists/mccracken/fred-databases) | [[9]](https://doi.org/10.20955/wp.2015.012) |
48
+ | [fred_qd_2025](./fred_qd_2025/README.md) | — | 1 | 65,170 | [link](https://www.stlouisfed.org/research/economists/mccracken/fred-databases) | [[10]](https://doi.org/10.20955/wp.2020.005) |
49
+ | [gvar](./gvar/README.md) | — | 33 | 52,866 | [link](https://data.mendeley.com/datasets/kfp5fhgkvf/1) | [[11]](https://doi.org/10.17863/CAM.104755) |
50
+ | [hermes](./hermes/README.md) | — | 10,000 | 5,220,000 | [link](https://github.com/etidav/HERMES) | [[12]](https://arxiv.org/abs/2202.03224) |
51
+ | [hierarchical_sales](./hierarchical_sales/README.md) | 1D, 1W | 118 | 215,350 / 30,680 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
52
+ | [hospital](./hospital/README.md) | — | 767 | 64,428 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
53
+ | [hospital_admissions](./hospital_admissions/README.md) | 1D, 1W | 8 | 13,846 / 1,968 | [link](https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024) | [[13]](https://doi.org/10.34740/kaggle/dsv/9992619) |
54
+ | [jena_weather](./jena_weather/README.md) | 10T, 1D, 1H | 1 | 1,106,784 / 7,686 / 184,464 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
55
+ | [kdd_cup_2022](./kdd_cup_2022/README.md) | 10T, 1D, 30T | 134 | 47,273,860 / 325,620 / 15,755,720 | [link](https://aistudio.baidu.com/competition/detail/152/0/task-definition) | [[14]](https://arxiv.org/abs/2208.04360) |
56
+ | [m5](./m5/README.md) | 1D, 1M, 1W | 30,490 | 428,849,460 / 13,805,685 / 60,857,703 | [link](https://www.kaggle.com/competitions/m5-forecasting-accuracy) | [[15]](https://doi.org/10.1016/j.ijforecast.2021.11.013) |
57
+ | [proenfo_bull](./proenfo_bull/README.md) | — | 41 | 2,877,216 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
58
+ | [proenfo_cockatoo](./proenfo_cockatoo/README.md) | — | 1 | 105,264 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
59
+ | [proenfo_gfc12](./proenfo_gfc12/README.md) | — | 11 | 867,108 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
60
+ | [proenfo_gfc14](./proenfo_gfc14/README.md) | — | 1 | 35,040 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
61
+ | [proenfo_gfc17](./proenfo_gfc17/README.md) | — | 8 | 280,704 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
62
+ | [proenfo_hog](./proenfo_hog/README.md) | — | 24 | 2,526,336 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
63
+ | [proenfo_pdb](./proenfo_pdb/README.md) | — | 1 | 35,040 | [link](https://github.com/Leo-VK/EnFoAV) | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
64
+ | [redset](./redset/README.md) | 15T, 1H, 5T | 126 | 1,052,371 / 283,070 / 2,960,408 | [link](https://github.com/amazon-science/redset/) | [[17]](https://www.amazon.science/publications/why-tpc-is-not-enough-an-analysis-of-the-amazon-redshift-fleet) |
65
+ | [restaurant](./restaurant/README.md) | — | 817 | 294,568 | [link](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting) | [[18]](https://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting/overview/citation) |
66
+ | [rohlik_orders](./rohlik_orders/README.md) | 1D, 1W | 7 | 115,650 / 15,316 | [link](https://www.kaggle.com/competitions/rohlik-orders-forecasting-challenge) | [[19]](https://www.kaggle.com/competitions/rohlik-orders-forecasting-challenge/overview/citation) |
67
+ | [rohlik_sales](./rohlik_sales/README.md) | 1D, 1W | 5,390 | 74,413,935 / 10,516,770 | [link](https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2) | [[20]](https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2/overview/citation) |
68
+ | [rossmann](./rossmann/README.md) | 1D, 1W | 1,115 | 7,352,310 / 889,770 | [link](https://www.kaggle.com/competitions/rossmann-store-sales) | [[21]](https://www.kaggle.com/competitions/rossmann-store-sales/overview/citation) |
69
+ | [solar](./solar/README.md) | 1D, 1W | 137 | 50,005 / 7,124 | [link](https://huggingface.co/datasets/Salesforce/GiftEval) | [[4]](https://arxiv.org/abs/2410.10393) |
70
+ | [solar_with_weather](./solar_with_weather/README.md) | 15T, 1H | 1 | 1,986,000 / 496,480 | [link](https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions) | — |
71
+ | [uci_air_quality](./uci_air_quality/README.md) | 1D, 1H | 1 | 5,057 / 121,641 | [link](https://archive.ics.uci.edu/dataset/360/air+quality) | [[22]](https://doi.org/10.24432/C59K5F) |
72
+ | [uk_covid_nation](./uk_covid_nation/README.md) | 1D, 1W | 4 | 41,216 / 5,936 | [link](https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed) | — |
73
+ | [uk_covid_utla](./uk_covid_utla/README.md) | 1D, 1W | 214 | 308,786 / 44,448 | [link](https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed) | — |
74
+ | [us_consumption](./us_consumption/README.md) | 1M, 1Q, 1Y | 31 | 24,552 / 8,122 / 1,984 | [link](https://apps.bea.gov/iTable/?reqid=19&step=3&isuri=1&nipa_table_list=2017&categories=underlying) | [[23]](https://doi.org/10.1016/j.ijforecast.2016.04.005) |
75
+ | [walmart](./walmart/README.md) | — | 2,936 | 4,609,143 | [link](https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting) | [[24]](https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting/overview/citation) |
76
+ | [world_co2_emissions](./world_co2_emissions/README.md) | — | 191 | 11,460 | [link](https://www.kaggle.com/datasets/ulrikthygepedersen/co2-emissions-by-country) | — |
77
+ | [world_life_expectancy](./world_life_expectancy/README.md) | — | 237 | 17,538 | [link](https://www.kaggle.com/datasets/nafayunnoor/global-life-expectancy-data-1950-2023) | [[25]](https://ourworldindata.org/life-expectancy#article-citation) |
78
+ | [world_tourism](./world_tourism/README.md) | — | 178 | 3,738 | [link](https://www.kaggle.com/datasets/bushraqurban/tourism-and-economic-impact) | [[26]](https://www.worldbank.org/en/archive/using-the-archives/terms-of-use-reproduction-and-citation) |
79
+
80
+ ## 读取示例
81
+
82
+ ```python
83
+ from tsfile import TsFileReader
84
+
85
+ reader = TsFileReader("<freq>.tsfile")
86
+ schemas = reader.get_all_table_schemas()
87
+ # 表名：<见各子集 README>；列见下方"列含义"。
88
+ ```
89
+
90
+ ## 引用
91
+
92
+ 原始合集 [fev-bench](https://arxiv.org/abs/2509.26468)：
93
+
94
+ ```bibtex
95
+ @article{shchur2025fev,
96
+ title={{fev-bench}: A Realistic Benchmark for Time Series Forecasting},
97
+ author={Shchur, Oleksandr and Ansari, Abdul Fatir and Turkmen, Caner and Stella, Lorenzo and Erickson, Nick and Guerron, Pablo and Bohlke-Schneider, Michael and Wang, Yuyang},
98
+ year={2025},
99
+ eprint={2509.26468},
100
+ archivePrefix={arXiv},
101
+ primaryClass={cs.LG}
102
+ }
103
+ ```
SZ_TAXI/15T/15T.tsfile ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ size 1899107
SZ_TAXI/1H/1H.tsfile ADDED
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+ version https://git-lfs.github.com/spec/v1
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