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BuildingsBenchComAmy/meta.json

@@ -0,0 +1,19 @@
+{
+    "name": "BuildingsBench900kComstockAmy2018",
+    "source": "https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-bench%2F",
+    "desp": "Simulated building energy consumption datasets, from comstock_amy2018.tar.gz.",
+    "domain": "Energy",
+    "license": "Apache",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": [
+        "h"
+    ],
+    "num_series": 2351,
+    "num_timesteps": 20592409,
+    "num_datapoints": 3040599260,
+    "split": "train",
+    "quality": "very low",
+    "timestamp_as_covariate": false
+}

BuildingsBenchComAmy/references.bib

@@ -0,0 +1,7 @@
+@inproceedings{Emami2023BuildingsBench,
+  author    = {Emami, Patrick and Sahu, Abhijeet and Graf, Peter},
+  title     = {BuildingsBench: {A} Large-Scale Dataset of 900k Buildings and Benchmark for Short-Term Load Forecasting},
+  booktitle = {Advances in Neural Information Processing Systems 36},
+  pages     = {19823--19857},
+  year      = {2023}
+}

FavoritaTransactionsKnownOil/meta.json

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+{
+    "name": "FavoritaTransactionsKnownOilPrice",
+    "source": "https://www.kaggle.com/c/favorita-grocery-sales-forecasting/data",
+    "desp": "In this dataset, you will be predicting the unit sales for thousands of items sold at different Favorita stores located in Ecuador. The training data includes dates, store and item information, whether that item was being promoted, as well as the unit sales. Additional files include supplementary information that may be useful in building your models.In this dataset, we assume oil price is known in advance!",
+    "domain": "Finance",
+    "license": "Unknown",
+    "targets": [
+        "transactions"
+    ],
+    "covariates": [
+        "holiday",
+        "dcoilwtico"
+    ],
+    "timestamp": "date",
+    "freq": [
+        "d"
+    ],
+    "num_series": 54,
+    "num_timesteps": 83488,
+    "num_datapoints": 250464,
+    "split": "train",
+    "quality": "very high",
+    "timestamp_as_covariate": true
+}

FavoritaTransactionsKnownOil/references.bib

@@ -0,0 +1,6 @@
+@article{favorita-grocery-sales-forecasting,
+  author    = {Corporación Favorita and Inversion and Julia Elliott and Mark McDonald},
+  title     = {Corporaci{\'o}n Favorita Grocery Sales Forecasting},
+  journal   = {Kaggle},
+  year      = {2017}
+}

GasSensorDynamic/meta.json

@@ -0,0 +1,37 @@
+{
+    "name": "GasSensorArrayUnderDynamicGasMixtures",
+    "source": "https://archive.ics.uci.edu/dataset/322/gas+sensor+array+under+dynamic+gas+mixtures",
+    "desp": "The data set contains the recordings of 16 chemical sensors exposed to two dynamic gas mixtures at varying concentrations. For each mixture, signals were acquired continuously during 12 hours.",
+    "domain": "Industry",
+    "license": "CC BY 4.0",
+    "targets": [
+        "sensor3",
+        "sensor4",
+        "sensor13",
+        "sensor11",
+        "sensor8",
+        "sensor2",
+        "sensor1",
+        "sensor6",
+        "sensor16",
+        "sensor10",
+        "sensor5",
+        "sensor12",
+        "sensor9",
+        "sensor14",
+        "sensor7",
+        "sensor15"
+    ],
+    "covariates": [
+        "Setpoint1",
+        "Setpoint2"
+    ],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 2,
+    "num_timesteps": 2097150,
+    "num_datapoints": 37748700,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": false
+}

GasSensorDynamic/references.bib

@@ -0,0 +1,6 @@
+@article{gas_sensor_array_under_dynamic_gas_mixtures_322,
+  author    = {Fonollosa, Jordi},
+  title     = {Gas Sensor Array under Dynamic Gas Mixtures},
+  journal   = {UCI Machine Learning Repository},
+  year      = {2015}
+}

MZVAV/meta.json

@@ -0,0 +1,17 @@
+{
+    "name": "MZVAV",
+    "source": "https://figshare.com/articles/dataset/LBNLDataSynthesisInventory_pdf/11752740",
+    "desp": "This data set can be used to evaluate and benchmark the performance accuracy of FDD algorithms or tools. It contains operational data from physical experimentation as well as simulation. The data sets currently cover AHU-VAV systems and RTUs, and will be expanded over time to include additional building systems and fault conditions. They include data from commonly available measurement points, spanning system operations under a variety of fault-present and fault-free conditions. This operational data is paired with ground truth information as to which faults are present during which time periods.",
+    "domain": "Others",
+    "license": "CC0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 6,
+    "num_timesteps": 398879,
+    "num_datapoints": 6832789,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": false
+}

MZVAV/references.bib

@@ -0,0 +1,6 @@
+@article{Granderson2020,
+    author = {Jessica Granderson and Guanjing Lin and Ari Harding and Piljae Im and Yan Chen},
+    title = {Dataset for building fault detection and diagnostics algorithm creation and performance testing},
+    journal = {Figshare},
+    year = {2020}
+}

Mdense/references.bib

@@ -0,0 +1,7 @@
+@article{de2020spatio,
+  title={A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction},
+  author={de Medrano, Rodrigo and Aznarte, Jose L},
+  journal={Applied Soft Computing},
+  volume={96},
+  year={2020}
+}

MelbournePedestrianCounts/meta.json

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+{
+    "name": "MelbournePedestrianCounts",
+    "source": "https://zenodo.org/records/4656626",
+    "desp": "This dataset contains hourly pedestrian counts captured from 66 sensors in Melbourne city starting from May 2009.",
+    "domain": "Others",
+    "license": "CC BY 4.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": "datetime",
+    "freq": [
+        "h"
+    ],
+    "num_series": 66,
+    "num_timesteps": 3132346,
+    "num_datapoints": 3132346,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": true
+}

MelbournePedestrianCounts/references.bib

@@ -0,0 +1,6 @@
+@article{godahewa_2020_4656626,
+  author    = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo},
+  title     = {Melbourne Pedestrian Counts Dataset},
+  journal   = {Zenodo},
+  year      = {2020}
+}

MetroTraffic/meta.json

@@ -0,0 +1,24 @@
+{
+    "name": "MetroTraffic",
+    "source": "https://archive.ics.uci.edu/dataset/492/metro+interstate+traffic+volume",
+    "desp": "Hourly Minneapolis-St Paul, MN traffic volume for westbound I-94.Includes weather and holiday features from 2012-2018.",
+    "domain": "Traffic",
+    "license": "CC BY 4.0",
+    "targets": [
+        "traffic_volume"
+    ],
+    "covariates": [
+        "clouds_all",
+        "rain_1h",
+        "temp",
+        "snow_1h"
+    ],
+    "timestamp": "date_time",
+    "freq": null,
+    "num_series": 1,
+    "num_timesteps": 48204,
+    "num_datapoints": 241020,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": true
+}

MetroTraffic/references.bib

@@ -0,0 +1,6 @@
+@article{metro_interstate_traffic_volume_492,
+  author    = {Hogue, John},
+  title     = {Metro Interstate Traffic Volume},
+  journal   = {UCI Machine Learning Repository},
+  year      = {2019}
+}

Metropt3/meta.json

@@ -0,0 +1,34 @@
+{
+    "name": "Metropt3",
+    "source": "https://archive.ics.uci.edu/dataset/791/metropt+3+dataset",
+    "desp": "From a metro train in an operational context, readings from pressure, temperature, motor current, and air intake valves were collected from a compressor's Air Production Unit (APU). This dataset reveals real predictive maintenance challenges encountered in the industry. It can be used for failure predictions, anomaly explanations, and other tasks.",
+    "domain": "Traffic",
+    "license": "CC BY 4.0",
+    "targets": [
+        "DV_pressure",
+        "Motor_current",
+        "Oil_temperature",
+        "TP2",
+        "TP3",
+        "Reservoirs",
+        "H1"
+    ],
+    "covariates": [
+        "COMP",
+        "DV_eletric",
+        "Towers",
+        "MPG",
+        "LPS",
+        "Pressure_switch",
+        "Oil_level",
+        "Caudal_impulses"
+    ],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 1,
+    "num_timesteps": 1048575,
+    "num_datapoints": 15728625,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": false
+}

Metropt3/references.bib

@@ -0,0 +1,6 @@
+@article{metropt-3_dataset_791,
+  author    = {Davari, Narjes and Veloso, Bruno and Ribeiro, Rita and Gama, Joao},
+  title     = {MetroPT-3 Dataset},
+  journal   = {UCI Machine Learning Repository},
+  year      = {2021}
+}

MiniApp/meta.json

@@ -0,0 +1,49 @@
+{
+    "name": "MiniApp",
+    "source": "https://github.com/zhanxingzhu/Functional_Relation_Field_Time_Series",
+    "desp": "Real-word flow data from two popular online payment MiniApps.",
+    "domain": "Others",
+    "license": "GPL-3.0",
+    "targets": [
+        "f2",
+        "f16",
+        "f1",
+        "f11",
+        "f19",
+        "f7",
+        "f8",
+        "f20",
+        "f17",
+        "f12",
+        "f18",
+        "f14",
+        "f10",
+        "f13",
+        "f15",
+        "f5",
+        "f4",
+        "f6",
+        "f9",
+        "f3"
+    ],
+    "covariates": [
+        "f21",
+        "f30",
+        "f25",
+        "f28",
+        "f27",
+        "f24",
+        "f22",
+        "f29",
+        "f26",
+        "f23"
+    ],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 2,
+    "num_timesteps": 12960,
+    "num_datapoints": 340416,
+    "split": "train",
+    "quality": "very high",
+    "timestamp_as_covariate": false
+}

MiniApp/references.bib

@@ -0,0 +1,7 @@
+@article{li2024functional,
+  title={Functional Relation Field: {A} Model-Agnostic Framework for Multivariate Time Series Forecasting},
+  author={Li, Ting and Yu, Bing and Li, Jianguo and Zhu, Zhanxing},
+  journal={Artificial Intelligence},
+  volume={334},
+  year={2024}
+}

MotionSense/meta.json

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+{
+    "name": "MotionSense",
+    "source": "https://github.com/mmalekzadeh/motion-sense",
+    "desp": "This dataset includes time-series data generated by accelerometer and gyroscope sensors (attitude, gravity, userAcceleration, and rotationRate). It is collected with an iPhone 6s kept in the participant's front pocket using SensingKit which collects information from Core Motion framework on iOS devices. All data collected in 50Hz sample rate. A total of 24 participants in a range of gender, age, weight, and height performed 6 activities in 15 trials in the same environment and conditions: downstairs, upstairs, walking, jogging, sitting, and standing.",
+    "domain": "Others",
+    "license": "MIT",
+    "targets": [
+        "x",
+        "y",
+        "z"
+    ],
+    "covariates": [],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 572,
+    "num_timesteps": 2474372,
+    "num_datapoints": 7423116,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": false
+}

MotionSense/references.bib

@@ -0,0 +1,7 @@
+@inproceedings{Malekzadeh:2019:MSD:3302505.3310068,
+    author = {Malekzadeh, Mohammad and Clegg, Richard G. and Cavallaro, Andrea and Haddadi, Hamed},
+    title = {Mobile Sensor Data Anonymization},
+    booktitle = {Proceedings of the 2019 International Conference on Internet of Things Design and Implementation},
+    year = {2019},
+    pages = {49--58}
+} 

MotorTemperature/meta.json

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+{
+    "name": "MotorTemperature",
+    "source": "https://www.kaggle.com/datasets/wkirgsn/electric-motor-temperature",
+    "desp": "The data set comprises several sensor data collected from a permanent magnet synchronous motor (PMSM) deployed on a test bench. The PMSM represents a german OEM's prototype model. Test bench measurements were collected by the LEA department at Paderborn University.",
+    "domain": "Others",
+    "license": "CC BY-SA 4.0",
+    "targets": [
+        "pm",
+        "stator_winding",
+        "stator_yoke",
+        "coolant",
+        "stator_tooth"
+    ],
+    "covariates": [
+        "torque",
+        "motor_speed",
+        "ambient",
+        "i_d",
+        "i_q",
+        "u_d",
+        "u_q"
+    ],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 69,
+    "num_timesteps": 1330816,
+    "num_datapoints": 15969792,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": false
+}

MotorTemperature/references.bib

@@ -0,0 +1,6 @@
+@article{wilhelm_kirchgassner_2021,
+  author    = {Wilhelm Kirchgässner and Oliver Wallscheid and Joachim B{\"o}cker},
+  title     = {Electric Motor Temperature},
+  journal   = {Kaggle},
+  year      = {2021}
+}

NAB/meta.json

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+{
+    "name": "NAB",
+    "source": "https://github.com/numenta/NAB?ref=hackernoon.com",
+    "desp": "This repository contains the data and scripts which comprise the Numenta Anomaly Benchmark (NAB) v1.1. NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is composed of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications.",
+    "domain": "Others",
+    "license": "MIT",
+    "targets": [
+        "value"
+    ],
+    "covariates": [],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 47,
+    "num_timesteps": 321206,
+    "num_datapoints": 321206,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": false
+}

NAB/references.bib

@@ -0,0 +1,6 @@
+@article{ahmad2017unsupervised,
+  author  = {Ahmad, Subutai and Lavin, Alexander and Purdy, Scott and Agha, Zuha},
+  title   = {Unsupervised real-time anomaly detection for streaming data},
+  journal = {Neurocomputing},
+  year    = {2017}
+}

NIFTYStock/meta.json

@@ -0,0 +1,28 @@
+{
+    "name": "NIFTYStock",
+    "source": "https://www.kaggle.com/datasets/rohanrao/nifty50-stock-market-data",
+    "desp": "Stock price data of the fifty stocks in NIFTY-50 index from NSE India. The data is the price history and trading volumes of the fifty stocks in the index NIFTY 50 from NSE (National Stock Exchange) India. All datasets are at a day-level with pricing and trading values split across .cvs files for each stock along with a metadata file with some macro-information about the stocks itself.",
+    "domain": "Finance",
+    "license": "CC0",
+    "targets": [
+        "Last",
+        "VWAP",
+        "Adj Close",
+        "Prev Close",
+        "Close",
+        "Volume",
+        "Low",
+        "High",
+        "Open",
+        "Turnover"
+    ],
+    "covariates": [],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 52,
+    "num_timesteps": 472259,
+    "num_datapoints": 4244406,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": false
+}

NIFTYStockKnownOpen/meta.json

@@ -0,0 +1,29 @@
+{
+    "name": "NIFTYStockKnownOpen",
+    "source": "https://www.kaggle.com/datasets/rohanrao/nifty50-stock-market-data",
+    "desp": "Stock price data of the fifty stocks in NIFTY-50 index from NSE India. The data is the price history and trading volumes of the fifty stocks in the index NIFTY 50 from NSE (National Stock Exchange) India. All datasets are at a day-level with pricing and trading values split across .cvs files for each stock along with a metadata file with some macro-information about the stocks itself. Assume open price is known.",
+    "domain": "Finance",
+    "license": "CC0",
+    "targets": [
+        "Last",
+        "VWAP",
+        "Adj Close",
+        "Prev Close",
+        "Close",
+        "Volume",
+        "Low",
+        "High",
+        "Turnover"
+    ],
+    "covariates": [
+        "Open"
+    ],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 51,
+    "num_timesteps": 472209,
+    "num_datapoints": 4244406,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": false
+}

NN5Daily/meta.json

@@ -0,0 +1,19 @@
+{
+    "name": "NN5Daily",
+    "source": "https://zenodo.org/records/4656110",
+    "desp": "This dataset was used in the NN5 forecasting competition. It contains 111 daily time series from the banking domain. The goal is predicting the daily cash withdrawals from ATMs in UK.",
+    "domain": "Finance",
+    "license": "CC BY 4.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": "datetime",
+    "freq": [
+        "d"
+    ],
+    "num_series": 1,
+    "num_timesteps": 791,
+    "num_datapoints": 87801,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": true
+}

NN5Daily/references.bib

@@ -0,0 +1,6 @@
+@article{godahewa_2020_4656110,
+  author    = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo},
+  title     = {NN5 Daily Dataset (with Missing Values)},
+  journal   = {Zenodo},
+  year      = {2020}
+}

OPSD-Household/meta.json

@@ -0,0 +1,21 @@
+{
+    "name": "OPSD-Household",
+    "source": "https://data.open-power-system-data.org/household_data/2020-04-15",
+    "desp": "This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. ",
+    "domain": "Energy",
+    "license": "MIT",
+    "targets": [],
+    "covariates": [],
+    "timestamp": "utc_timestamp",
+    "freq": [
+        "15min",
+        "min",
+        "h"
+    ],
+    "num_series": 33,
+    "num_timesteps": 7121240,
+    "num_datapoints": 47880484,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": true
+}

OPSD-PV-Wind/meta.json

@@ -0,0 +1,19 @@
+{
+    "name": "OPSD-PV-Wind",
+    "source": "https://data.open-power-system-data.org/ninja_pv_wind_profiles/2020-09-16",
+    "desp": "This data package contains simulated wind and PV capacity factors from Renewables.ninja, at hourly resolution, for all European countries. Unlike the time series data package, which contains data reported from network operators, this package contains simulated data using historical weather conditions.",
+    "domain": "Energy",
+    "license": "CC BY-NC 4.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": [
+        "h"
+    ],
+    "num_series": 36,
+    "num_timesteps": 12623040,
+    "num_datapoints": 48738960,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": false
+}

OPSD-PV-Wind/references.bib

@@ -0,0 +1,17 @@
+@article{Pfenninger2016PV,
+  author  = {Pfenninger, Stefan and Staffell, Iain},
+  title   = {Long-term Patterns of European {PV} Output Using 30 years of Validated Hourly Reanalysis and Satellite Data},
+  journal = {Energy},
+  volume  = {114},
+  pages   = {1251--1265},
+  year    = {2016}
+}
+
+@article{Staffell2016Wind,
+  author  = {Staffell, Iain and Pfenninger, Stefan},
+  title   = {Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output},
+  journal = {Energy},
+  volume  = {114},
+  pages   = {1224--1239},
+  year    = {2016}
+}

OPSD-When2Heat/meta.json

@@ -0,0 +1,19 @@
+{
+    "name": "OPSD-When2Heat",
+    "source": "https://data.open-power-system-data.org/when2heat/2023-07-27",
+    "desp": "This dataset comprises national time series for representing building heat pumps in power system models. The heat demand of buildings and the coefficient of performance (COP) of heat pumps is calculated for 28 European countries from 2008 to 2022 in an hourly resolution. Heat demand time series for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. The profiles are year-wise scaled to 1 TWh each. For the years 2008 to 2015, the data is additionally scaled with annual statistics on the final energy consumption for heating. COP time series for different heat sources �C air, ground, and groundwater using reanalysis temperature data, spatially aggregated with respect to the heat demand, and corrected based on field measurements. ",
+    "domain": "Energy",
+    "license": "CC BY-NC 4.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": "utc_timestamp",
+    "freq": [
+        "h"
+    ],
+    "num_series": 28,
+    "num_timesteps": 2086138,
+    "num_datapoints": 45614480,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": true
+}

OPSD-When2Heat/references.bib

@@ -0,0 +1,8 @@
+@article{ruhnau2019heatdemand,
+  author  = {Ruhnau, Oliver and Hirth, Lion and Praktiknjo, Aaron},
+  title   = {Time series of heat demand and heat pump efficiency for energy system modeling},
+  journal = {Scientific Data},
+  volume  = {6},
+  pages   = {189},
+  year    = {2019}
+}

OPSD/meta.json

@@ -0,0 +1,19 @@
+{
+    "name": "OPSD",
+    "source": "https://data.open-power-system-data.org/",
+    "desp": "This data package contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. The data is aggregated either by country, control area or bidding zone. Geographical coverage includes the EU and some neighbouring countries. All variables are provided in hourly resolution. Where original data is available in higher resolution (half-hourly or quarter-hourly), it is provided in separate files. This package version only contains data provided by TSOs and power exchanges via ENTSO-E Transparency, covering the period 2015-mid 2020. See previous versions for historical data from a broader range of sources.",
+    "domain": "Energy",
+    "license": "MIT",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": [
+        "h"
+    ],
+    "num_series": 80,
+    "num_timesteps": 2859861,
+    "num_datapoints": 22899412,
+    "split": "train",
+    "quality": "very high",
+    "timestamp_as_covariate": false
+}

OccupancyDetection/meta.json

@@ -0,0 +1,25 @@
+{
+    "name": "OccupancyDetection",
+    "source": "https://archive.ics.uci.edu/dataset/357/occupancy+detection",
+    "desp": "Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute.",
+    "domain": "Industry",
+    "license": "CC BY 4.0",
+    "targets": [
+        "CO2",
+        "Temperature"
+    ],
+    "covariates": [
+        "Occupancy",
+        "Light",
+        "Humidity",
+        "HumidityRatio"
+    ],
+    "timestamp": "date",
+    "freq": null,
+    "num_series": 3,
+    "num_timesteps": 20560,
+    "num_datapoints": 123360,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": true
+}

OccupancyDetection/references.bib

@@ -0,0 +1,6 @@
+@article{occupancy_detection__357,
+  author    = {Candanedo, Luis},
+  title     = {Occupancy Detection},
+  journal   = {UCI Machine Learning Repository},
+  year      = {2016}
+}

OikolabWeather/meta.json

@@ -0,0 +1,28 @@
+{
+    "name": "OikolabWeather",
+    "source": "https://zenodo.org/records/5184708",
+    "desp": "This dataset was kindly provided by OikoLab (https://oikolab.com). It contains eight time series representing the hourly climate data nearby Monash University, Clayton, Victoria, Australia from 2010-01-01 to 2021-05-31. The climate data include temperature (C), dewpoint temperature (C), wind speed (m/s), mean sea level pressure (Pa), relative humidity (0-1), surface solar radiation (W/m^2), surface thermal radiation (W/m^2) and total cloud cover (0-1).",
+    "domain": "Environment",
+    "license": "CC BY 4.0",
+    "targets": [
+        "mean_sea_level_pressure",
+        "temperature",
+        "surface_solar_radiation",
+        "dewpoint_temperature",
+        "relative_humidity",
+        "total_cloud_cover",
+        "surface_thermal_radiation",
+        "wind_speed"
+    ],
+    "covariates": [],
+    "timestamp": null,
+    "freq": [
+        "h"
+    ],
+    "num_series": 1,
+    "num_timesteps": 100057,
+    "num_datapoints": 800456,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": false
+}

OikolabWeather/references.bib

@@ -0,0 +1,6 @@
+@article{godahewa_2021_5184708,
+  author    = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo},
+  title     = {Oikolab Weather Dataset},
+  journal   = {Zenodo},
+  year      = {2021}
+}

OilWell/meta.json

@@ -0,0 +1,17 @@
+{
+    "name": "OilWell",
+    "source": "https://www.kaggle.com/datasets/afrniomelo/3w-dataset",
+    "desp": "A realistic and public dataset with rare undesirable real events in oil wells.",
+    "domain": "Energy",
+    "license": "CC BY 4.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 1984,
+    "num_timesteps": 50913215,
+    "num_datapoints": 244525350,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": false
+}

OilWell/references.bib

@@ -0,0 +1,7 @@
+@article{vargas2019realistic,
+  title={A realistic and public dataset with rare undesirable real events in oil wells},
+  author={Vargas, Ricardo Emanuel Vaz and Munaro, Celso Jos{\'e} and Ciarelli, Patrick Marques and Medeiros, Andr{\'e} Gon{\c{c}}alves and do Amaral, Bruno Guberfain and Barrionuevo, Daniel Centurion and de Ara{\'u}jo, Jean Carlos Dias and Ribeiro, Jorge Lins and Magalh{\~a}es, Lucas Pierezan},
+  journal={Journal of Petroleum Science and Engineering},
+  volume={181},
+  year={2019}
+}

PAMAP2/meta.json

@@ -0,0 +1,62 @@
+{
+    "name": "PAMAP2",
+    "source": "https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring",
+    "desp": "The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities (such as walking, cycling, playing soccer, etc.), performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor. The dataset can be used for activity recognition and intensity estimation, while developing and applying algorithms of data processing, segmentation, feature extraction and classification.",
+    "domain": "Others",
+    "license": "CC BY 4.0",
+    "targets": [
+        "accelerometer_16_y",
+        "temperature.1",
+        "accelerometer_6_y.1",
+        "accelerometer_16_x",
+        "accelerometer_16_y.1",
+        "magnetometer_x.2",
+        "gyroscope_z.2",
+        "accelerometer_6_y.2",
+        "accelerometer_16_z.2",
+        "magnetometer_x.1",
+        "accelerometer_6_z.2",
+        "magnetometer_z",
+        "gyroscope_x.2",
+        "temperature.2",
+        "gyroscope_y",
+        "magnetometer_y.1",
+        "magnetometer_x",
+        "accelerometer_6_y",
+        "accelerometer_16_y.2",
+        "magnetometer_y",
+        "gyroscope_z",
+        "magnetometer_z.1",
+        "accelerometer_16_z.1",
+        "accelerometer_6_x.1",
+        "gyroscope_x",
+        "accelerometer_6_x",
+        "gyroscope_y.1",
+        "accelerometer_16_x.2",
+        "magnetometer_y.2",
+        "gyroscope_z.1",
+        "heartrate",
+        "accelerometer_16_x.1",
+        "accelerometer_16_z",
+        "accelerometer_6_z",
+        "magnetometer_z.2",
+        "accelerometer_6_x.2",
+        "gyroscope_x.1",
+        "temperature",
+        "accelerometer_6_z.1",
+        "gyroscope_y.2"
+    ],
+    "covariates": [
+        "activity"
+    ],
+    "timestamp": null,
+    "freq": [
+        "10ms"
+    ],
+    "num_series": 9,
+    "num_timesteps": 2724953,
+    "num_datapoints": 111723073,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": false
+}

PAMAP2/references.bib

@@ -0,0 +1,6 @@
+@article{reiss2012pamap2,
+  author  = {Reiss, Attila},
+  title   = {PAMAP2 Physical Activity Monitoring},
+  journal = {UCI Machine Learning Repository},
+  year    = {2012}
+}

PEMS-Bay-METRO-LA/meta.json

@@ -0,0 +1,19 @@
+{
+    "name": "PEMS-Bay-METRO-LA",
+    "source": "https://github.com/liyaguang/DCRNN",
+    "desp": "The traffic flows for Los Angeles (METR-LA) and the Bay Area (PEMS-BAY).",
+    "domain": "Traffic",
+    "license": "MIT",
+    "targets": [],
+    "covariates": [],
+    "timestamp": "timestamp",
+    "freq": [
+        "5min"
+    ],
+    "num_series": 2,
+    "num_timesteps": 86388,
+    "num_datapoints": 24032004,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": true
+}

PEMS-Bay-METRO-LA/references.bib

@@ -0,0 +1,6 @@
+@inproceedings{li2018dcrnn_traffic,
+  title={Diffusion Convolutional Recurrent Neural Network: {D}ata-Driven Traffic Forecasting},
+  author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
+  booktitle={Proceedings of the 2018 International Conference on Learning Representations},
+  year={2018}
+}

PEMSCalifornia/meta.json

@@ -0,0 +1,17 @@
+{
+    "name": "PEMSCalifornia",
+    "source": "https://github.com/LibCity/Bigscity-LibCity-Datasets",
+    "desp": "Traffic flows in California cities.",
+    "domain": "Traffic",
+    "license": "Apache-2.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 8,
+    "num_timesteps": 105726,
+    "num_datapoints": 38220894,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": false
+}

PEMSCalifornia/references.bib

@@ -0,0 +1,7 @@
+@inproceedings{10.1145/3474717.3483923,
+  author = {Wang, Jingyuan and Jiang, Jiawei and Jiang, Wenjun and Li, Chao and Zhao, Wayne Xin},
+  title = {LibCity: {A}n Open Library for Traffic Prediction},
+  year = {2021},
+  booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems},
+  pages = {145--148},
+}

PM25FiveCities/meta.json

@@ -0,0 +1,19 @@
+{
+    "name": "PM25FiveCities",
+    "source": "https://archive.ics.uci.edu/dataset/394/pm2+5+data+of+five+chinese+cities",
+    "desp": "This hourly data set contains the PM2.5 data in Beijing, Shanghai, Guangzhou, Chengdu and Shenyang. Meanwhile, meteorological data for each city are also included.",
+    "domain": "Environment",
+    "license": "CC BY 4.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": "timestamp",
+    "freq": [
+        "h"
+    ],
+    "num_series": 5,
+    "num_timesteps": 112920,
+    "num_datapoints": 1151784,
+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": true
+}

PM25FiveCities/references.bib

@@ -0,0 +1,6 @@
+@article{chen2016pm25,
+  author  = {Chen, Song},
+  title   = {PM2.5 Data of Five Chinese Cities},
+  journal = {UCI Machine Learning Repository},
+  year    = {2016}
+}

PUMP/meta.json

@@ -0,0 +1,17 @@
+{
+    "name": "PUMP",
+    "source": "https://www.kaggle.com/datasets/nphantawee/pump-sensor-data",
+    "desp": "Sensor readings from a water pump of a small area far from big town, there are 7 system failure in test dataset.",
+    "domain": "Industry",
+    "license": "Unknown",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": null,
+    "num_series": 2,
+    "num_timesteps": 220302,
+    "num_datapoints": 9693288,
+    "split": "train",
+    "quality": "medium",
+    "timestamp_as_covariate": false
+}

ProEnFo/meta.json

@@ -0,0 +1,19 @@
+{
+    "name": "ProEnFo",
+    "source": "https://github.com/Leo-VK/EnFoAV",
+    "desp": "Dataset from the paper Benchmarks and Custom Package for Electrical Load Forecasting. The dataset contains load and external variables like air temperature.",
+    "domain": "Industry",
+    "license": "BSD 3-Clause",
+    "targets": [],
+    "covariates": [],
+    "timestamp": "datetime",
+    "freq": [
+        "h"
+    ],
+    "num_series": 9,
+    "num_timesteps": 229126,
+    "num_datapoints": 5312254,
+    "split": "train",
+    "quality": "very high",
+    "timestamp_as_covariate": true
+}

ProEnFo/references.bib

@@ -0,0 +1,6 @@
+@article{wang2023benchmarks,
+  title={Benchmarks and Custom Package for Energy Forecasting},
+  author={Wang, Zhixian and Wen, Qingsong and Zhang, Chaoli and Sun, Liang and Von Krannichfeldt, Leandro and Pan, Shirui and Wang, Yi},
+  journal={arXiv preprint arXiv:2307.07191},
+  year={2023}
+}