Time Series Datasets¶
RealSeries provides several example datasets that can be used for Forecast with Uncertainty, and Anomaly Detection, Granger causality.
Forecast Datasets¶
Dir |
Name |
External |
channel |
HNN |
MC_dropout |
CRMMD |
|||
RMSE |
EPIW |
RMSE |
EPIW |
RMSE |
EPIW |
||||
Forecast_data |
air_quality |
muti |
79.60 |
0.058 |
81.16 |
0.339 |
80.69 |
0.010 |
|
bike_sharing |
https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset |
muti |
40.71 |
0.054 |
38.86 |
0.258 |
37.93 |
0.006 |
|
metro_traffic |
https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volum |
muti |
556.3 |
0.102 |
523.6 |
0.304 |
545.5 |
0.017 |
|
pm25 |
muti |
58.81 |
0.022 |
70.95 |
0.331 |
57.43 |
0.010 |
||
Anomaly Detection Datasets¶
Dir |
Name |
External |
paper |
channel |
Lumino |
SR_CNN |
IForest |
LSTM_dym |
Rrcf |
VAE |
||||||||||||
pre |
rec |
f1 |
pre |
rec |
f1 |
pre |
rec |
f1 |
pre |
rec |
f1 |
pre |
rec |
f1 |
pre |
rec |
f1 |
|||||
realKnownCause |
nyc_taxi |
https://github.com/numenta/NAB/tree/master/data/realKnownCause |
single |
0.99 |
0.19 |
0.33 |
0.99 |
0.59 |
0.75 |
0.99 |
0.39 |
0.57 |
0.99 |
0.39 |
0.57 |
0.99 |
0.19 |
0.33 |
0.99 |
0.79 |
0.88 |
|
realTweets |
UPS |
single |
0 |
0 |
0 |
0.98 |
0.99 |
0.99 |
0 |
0 |
0 |
0.73 |
0.99 |
0.95 |
0 |
0 |
0 |
0.85 |
0.99 |
0.92 |
||
FE |
single |
0 |
0 |
0 |
0.37 |
0 |
0.01 |
0.98 |
0.99 |
0.99 |
0.87 |
0.99 |
0.93 |
0.99 |
0.99 |
0.99 |
0.96 |
0.99 |
0.98 |
|||
KO |
single |
0 |
0 |
0 |
0.99 |
0.49 |
0.66 |
0.99 |
0.49 |
0.66 |
0 |
0 |
0 |
0.99 |
0.01 |
0.01 |
0 |
0 |
0 |
|||
IBM |
single |
0 |
0 |
0 |
0.99 |
0.99 |
0.99 |
0 |
0 |
0 |
0.99 |
0.61 |
0.76 |
0 |
0 |
0 |
0 |
0 |
0 |
|||
GOOG |
single |
0 |
0 |
0 |
0 |
0 |
0 |
0.99 |
0.99 |
0.99 |
0 |
0 |
0 |
0 |
0 |
0 |
0.99 |
0.99 |
0.99 |
|||
FB |
single |
0 |
0 |
0 |
0.99 |
0.99 |
0.99 |
0 |
0 |
0 |
0.85 |
0.99 |
0.92 |
0 |
0 |
0 |
0 |
0 |
0 |
|||
CVS |
single |
0.996 |
0.08 |
0.14 |
0 |
0 |
0 |
0.99 |
0.99 |
0.99 |
0 |
0 |
0 |
0 |
0 |
0 |
0.99 |
0.75 |
0.86 |
|||
CRM |
single |
0 |
0 |
0 |
0 |
0 |
0 |
0.99 |
0.99 |
0.99 |
0.91 |
0.99 |
0.95 |
0 |
0 |
0 |
0.89 |
0.99 |
0.94 |
|||
AMZN |
single |
0.99 |
0.49 |
0.66 |
0 |
0 |
0 |
0.99 |
0.99 |
0.99 |
0 |
0 |
0 |
0 |
0 |
0 |
0.98 |
0.99 |
0.99 |
|||
AAPL |
single |
0 |
0 |
0 |
0.98 |
0.99 |
0.99 |
0.98 |
0.99 |
0.99 |
0 |
0 |
0 |
0.99 |
0.99 |
0.99 |
0.97 |
0.99 |
0.98 |
|||
Yahoo |
A1 |
https://yahooresearch.tumblr.com/post/114590420346/a-benchmark-dataset-for-time-series-anomaly |
single |
0.86 |
0.57 |
0.69 |
0.83 |
0.17 |
0.29 |
0.54 |
0.01 |
0.02 |
0.003 |
0.008 |
0.005 |
0.77 |
0.64 |
0.70 |
||||
NASA |
multi |
0.96 |
0.15 |
0.26 |
0.83 |
0.50 |
0.62 |
|||||||||||||||
Granger causality Datasets¶
datasets |
External |
GC |
DWGC |
|||
accuracy |
recall |
accuracy |
recall |
|||
NAR simulation |
window length=10 |
0.42 |
0.58 |
0.44 |
0.73 |
|
window length=20 |
0.76 |
0.65 |
0.80 |
0.65 |
||
window length=30 |
0.93 |
0.66 |
0.94 |
0.67 |
||
window length=100 |
1 |
0.86 |
1 |
0.88 |
||
ENSO data(DWGC) |
Notebook: http://git.real-ai.cn/realseries/realseries/blob/causality_branch/notebooks/DWGC.ipynb |
|||||
ENSO data(GC) |
Notebook: http://git.real-ai.cn/realseries/realseries/blob/causality_branch/notebooks/GC.ipynb |
|||||