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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"
+    ],
+    "num_series": 3594,
+    "num_timesteps": 22254920,
+    "num_datapoints": 39639306,
+    "split": "train",
+    "quality": "medium",
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+}
\ No newline at end of file
diff --git a/BuildingsBenchRealCSV/references.bib b/BuildingsBenchRealCSV/references.bib
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--- /dev/null
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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}
+}
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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": [
+        "close"
+    ],
+    "covariates": [
+        "open",
+        "high",
+        "low",
+        "volume",
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+    ],
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+{
+    "name": "FavoritaSales",
+    "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",
+    "targets": [],
+    "covariates": [],
+    "timestamp": "date",
+    "freq": [
+        "d",
+        "2d"
+    ],
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+    "split": "train",
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+}
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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}
+}
\ No newline at end of file
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+    "name": "LargeST",
+    "source": "https://www.kaggle.com/datasets/liuxu77/largest",
+    "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).",
+    "domain": "Others",
+    "license": "CC BY-NC 4.0",
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+    "name": "LondonSmartMeters",
+    "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",
+    "license": "CC BY 4.0",
+    "targets": [],
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+    "timestamp": "datetime",
+    "freq": [
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+@article{godahewa_2020_4656072,
+  author    = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo},
+  title     = {London Smart Meters Dataset (with Missing Values)},
+  journal   = {Zenodo},
+  year      = {2020}
+}
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+    "source": "https://zenodo.org/communities/forecasting/records?q=&f=subject%3AM4&l=list&p=1&s=10&sort=newest",
+    "desp": "This dataset contains time series used in the M4 forecasting competition.The series are belonging to 6 different domains: demographic, micro, macro, industry, finance and other.",
+    "domain": "Others",
+    "license": "CC BY 4.0",
+    "targets": [],
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+}
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+    "name": "M5",
+    "source": "https://www.kaggle.com/c/m5-forecasting-accuracy/overview",
+    "desp": "In this competition, the fifth iteration, you will use hierarchical sales data from Walmart, the world's largest company by revenue, to forecast daily sales for the next 28 days. The data, covers stores in three US States (California, Texas, and Wisconsin) and includes item level, department, product categories, and store details. In addition, it has explanatory variables such as price, promotions, day of the week, and special events. Together, this robust dataset can be used to improve forecasting accuracy.",
+    "domain": "Finance",
+    "license": "Unknown",
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+    "split": "train",
+    "quality": "high",
+    "timestamp_as_covariate": false
+}
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+@article{m5-forecasting-accuracy,
+  author    = {Howard, Addison and Makridakis, Spyros and Vangelis},
+  title     = {M5 Forecasting -- Accuracy},
+  journal   = {Kaggle},
+  year      = {2020}
+}
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+    "name": "ResidentialPower",
+    "source": "https://zenodo.org/records/8219786",
+    "desp": "The Residential Power and Battery data is an open-source dataset designed to facilitate the advancement of predictive and optimisation algorithms. It features anonymised, minute-by-minute real-world customer data on energy consumption, solar generation, and battery measurements. This dataset was compiled by SwitchDin and made available through Monash University on the Zenodo platform.",
+    "domain": "Energy",
+    "license": "CC BY 4.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": [
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diff --git a/ResidentialPower/references.bib b/ResidentialPower/references.bib
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index 0000000000000000000000000000000000000000..fea5a5334dd29cfb3b0e8e80aa321c28d0e105b9
--- /dev/null
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+@article{bergmeir_2023_8219786,
+  author    = {Bergmeir, Christoph and Bui, Quang and de Nijs, Frits and Stuckey, Peter},
+  title     = {Residential Power and Battery Data},
+  journal   = {Zenodo},
+  year      = {2023}
+}
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+{
+    "name": "Subseasonal",
+    "source": "https://github.com/microsoft/subseasonal_data",
+    "desp": "SubseasonalClimateUSA dataset.",
+    "domain": "Environment",
+    "license": "MIT",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": [
+        "d"
+    ],
+    "num_series": 267,
+    "num_timesteps": 93791421,
+    "num_datapoints": 5668668095,
+    "split": "train",
+    "quality": "low",
+    "timestamp_as_covariate": false
+}
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+@inproceedings{mouatadid2023subseasonal,
+  title = {SubseasonalClimateUSA: {A} Dataset for Subseasonal Forecasting and Benchmarking},
+  author = {Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, and Lester Mackey},
+  booktitle = {Advances in Neural Information Processing Systems 36},
+  year = {2023}
+}
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+{
+    "name": "Weather",
+    "source": "https://zenodo.org/records/4654822",
+    "desp": "This dataset contains 3010 daily time series representing the variations of four weather variables: rain, mintemp, maxtemp and solar radiation, measured at the weather stations in Australia. ",
+    "domain": "Environment",
+    "license": "CC BY 4.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": [
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+    "split": "train",
+    "quality": "medium",
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+}
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+++ b/Weather/references.bib
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+@article{godahewa2020weather,
+  title   = {Weather Dataset},
+  author  = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Montero-Manso, Pablo and Hyndman, Rob},
+  journal = {Zenodo},
+  year    = {2020}
+}
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+{
+    "name": "WikiTrafficDaily",
+    "source": "https://zenodo.org/records/4656075",
+    "desp": "This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10.",
+    "domain": "Others",
+    "license": "CC BY 4.0",
+    "targets": [],
+    "covariates": [],
+    "timestamp": null,
+    "freq": [
+        "d"
+    ],
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+    "num_datapoints": 304478388,
+    "split": "train",
+    "quality": "medium",
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+}
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