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BuildingsBenchRealCSV/meta.json

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+{
+    "name": "BuildingsBenchRealCSV",
+    "source": "https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-bench%2F",
+    "desp": "Real building energy consumption datasets.",
+    "domain": "Energy",
+    "license": "Apache",
+    "targets": [],
+    "covariates": [],
+    "timestamp": "timestamp",
+    "freq": [
+        "h"
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+}

BuildingsBenchRealCSV/references.bib

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+@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}
+}

CSI500/meta.json

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+    "name": "CSI500",
+    "source": "https://www.csindex.com.cn/#/indices/family/detail?indexCode=000500",
+    "desp": "This dataset contains multivariate daily data of stocks from the Chinese stock market that are constituents of the CSI 300 indices.",
+    "domain": "Finance",
+    "license": "Unknown",
+    "targets": [
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+    "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.",
+    "domain": "Finance",
+    "license": "Unknown",
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FavoritaSales/references.bib

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+@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}
+}

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+    "desp": "LargeST comprises four sub-datasets, each characterized by a different number of sensors. The biggest one is California (CA), including a total number of 8,600 sensors. We also construct three subsets of CA by selecting three representative areas within CA and forming the sub-datasets of Greater Los Angeles (GLA), Greater Bay Area (GBA), and San Diego (SD).",
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+    "source": "https://zenodo.org/records/4656072",
+    "desp": "Kaggle London Smart Meters dataset contains 5560 half hourly time series that represent the energy consumption readings of London households in kilowatt hour (kWh) from November 2011 to February 2014.",
+    "domain": "Energy",
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LondonSmartMeters/references.bib

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+  author    = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo},
+  title     = {London Smart Meters Dataset (with Missing Values)},
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+  year      = {2020}
+}

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+    "domain": "Others",
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