Datasets¶
All datasets used in the skforecast library and related tutorials are accessible using the skforecast.datasets.fetch_dataset() function.
Each dataset in this collection comes with a description of its time series and a reference to its original source.
Available data sets are stored at skforecast-datasets and can be easily listed using the skforecast.datasets.show_datasets_info() function.
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# Libraries
# ==============================================================================
from skforecast.datasets import fetch_dataset, show_datasets_info
# Libraries
# ==============================================================================
from skforecast.datasets import fetch_dataset, show_datasets_info
By default, the data is structured as a pandas dataframe with a datetime index and frequency. Additionally, a concise description is printed for quick reference.
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# Download data
# ==============================================================================
data = fetch_dataset(name="bike_sharing")
data.head()
# Download data
# ==============================================================================
data = fetch_dataset(name="bike_sharing")
data.head()
╭───────────────────────────────── bike_sharing ──────────────────────────────────╮ │ Description: │ │ Hourly usage of the bike share system in the city of Washington D.C. during the │ │ years 2011 and 2012. In addition to the number of users per hour, information │ │ about weather conditions and holidays is available. │ │ │ │ Source: │ │ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository. │ │ https://doi.org/10.24432/C5W894. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/bike_sharing_dataset_clean.csv │ │ │ │ Shape: 17544 rows x 11 columns │ ╰─────────────────────────────────────────────────────────────────────────────────╯
Out[2]:
| holiday | workingday | weather | temp | atemp | hum | windspeed | users | month | hour | weekday | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| date_time | |||||||||||
| 2011-01-01 00:00:00 | 0.0 | 0.0 | clear | 9.84 | 14.395 | 81.0 | 0.0 | 16.0 | 1 | 0 | 5 |
| 2011-01-01 01:00:00 | 0.0 | 0.0 | clear | 9.02 | 13.635 | 80.0 | 0.0 | 40.0 | 1 | 1 | 5 |
| 2011-01-01 02:00:00 | 0.0 | 0.0 | clear | 9.02 | 13.635 | 80.0 | 0.0 | 32.0 | 1 | 2 | 5 |
| 2011-01-01 03:00:00 | 0.0 | 0.0 | clear | 9.84 | 14.395 | 75.0 | 0.0 | 13.0 | 1 | 3 | 5 |
| 2011-01-01 04:00:00 | 0.0 | 0.0 | clear | 9.84 | 14.395 | 75.0 | 0.0 | 1.0 | 1 | 4 | 5 |
Downloading raw data, without any preprocessing, is possible by specifying the raw=True argument.
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# Download raw data
# ==============================================================================
data = fetch_dataset(name="bike_sharing", raw=True)
data.head()
# Download raw data
# ==============================================================================
data = fetch_dataset(name="bike_sharing", raw=True)
data.head()
╭───────────────────────────────── bike_sharing ──────────────────────────────────╮ │ Description: │ │ Hourly usage of the bike share system in the city of Washington D.C. during the │ │ years 2011 and 2012. In addition to the number of users per hour, information │ │ about weather conditions and holidays is available. │ │ │ │ Source: │ │ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository. │ │ https://doi.org/10.24432/C5W894. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/bike_sharing_dataset_clean.csv │ │ │ │ Shape: 17544 rows x 12 columns │ ╰─────────────────────────────────────────────────────────────────────────────────╯
Out[3]:
| date_time | holiday | workingday | weather | temp | atemp | hum | windspeed | users | month | hour | weekday | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 0.0 | 0.0 | clear | 9.84 | 14.395 | 81.0 | 0.0 | 16.0 | 1 | 0 | 5 |
| 1 | 2011-01-01 01:00:00 | 0.0 | 0.0 | clear | 9.02 | 13.635 | 80.0 | 0.0 | 40.0 | 1 | 1 | 5 |
| 2 | 2011-01-01 02:00:00 | 0.0 | 0.0 | clear | 9.02 | 13.635 | 80.0 | 0.0 | 32.0 | 1 | 2 | 5 |
| 3 | 2011-01-01 03:00:00 | 0.0 | 0.0 | clear | 9.84 | 14.395 | 75.0 | 0.0 | 13.0 | 1 | 3 | 5 |
| 4 | 2011-01-01 04:00:00 | 0.0 | 0.0 | clear | 9.84 | 14.395 | 75.0 | 0.0 | 1.0 | 1 | 4 | 5 |
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# Show dataset information
# ==============================================================================
show_datasets_info(datasets_names=None) # None means all datasets
# Show dataset information
# ==============================================================================
show_datasets_info(datasets_names=None) # None means all datasets
╭────────────────────────────── air_quality_valencia ──────────────────────────────╮ │ Description: │ │ Hourly measures of several air chemical pollutant at Valencia city (Avd. │ │ Francia) from 2019-01-01 to 20213-12-31. Including the following variables: │ │ pm2.5 (µg/m³), CO (mg/m³), NO (µg/m³), NO2 (µg/m³), PM10 (µg/m³), NOx (µg/m³), │ │ O3 (µg/m³), Veloc. (m/s), Direc. (degrees), SO2 (µg/m³). │ │ │ │ Source: │ │ Red de Vigilancia y Control de la Contaminación Atmosférica, 46250047-València - │ │ Av. França, https://mediambient.gva.es/es/web/calidad-ambiental/datos- │ │ historicos. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/air_quality_valencia.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────── air_quality_valencia_no_missing ─────────────────────────╮ │ Description: │ │ Hourly measures of several air chemical pollutant at Valencia city (Avd. │ │ Francia) from 2019-01-01 to 20213-12-31. Including the following variables: │ │ pm2.5 (µg/m³), CO (mg/m³), NO (µg/m³), NO2 (µg/m³), PM10 (µg/m³), NOx (µg/m³), │ │ O3 (µg/m³), Veloc. (m/s), Direc. (degrees), SO2 (µg/m³). Missing values have │ │ been imputed using linear interpolation. │ │ │ │ Source: │ │ Red de Vigilancia y Control de la Contaminación Atmosférica, 46250047-València - │ │ Av. França, https://mediambient.gva.es/es/web/calidad-ambiental/datos- │ │ historicos. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/air_quality_valencia_no_missing.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────────── ashrae_daily ──────────────────────────────────╮ │ Description: │ │ Daily energy consumption data from the ASHRAE competition with building metadata │ │ and weather data. │ │ │ │ Source: │ │ Kaggle competition Addison Howard, Chris Balbach, Clayton Miller, Jeff Haberl, │ │ Krishnan Gowri, Sohier Dane. (2019). ASHRAE - Great Energy Predictor III. │ │ Kaggle. https://www.kaggle.com/c/ashrae-energy-prediction/overview │ │ │ │ URL: │ │ https://drive.google.com/file/d/1fMsYjfhrFLmeFjKG3jenXjDa5s984ThC/view?usp=shari │ │ ng │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────────────── australia_tourism ────────────────────────────────╮ │ Description: │ │ Quarterly overnight trips (in thousands) from 1998 Q1 to 2016 Q4 across │ │ Australia. The tourism regions are formed through the aggregation of Statistical │ │ Local Areas (SLAs) which are defined by the various State and Territory tourism │ │ authorities according to their research and marketing needs. │ │ │ │ Source: │ │ Wang, E, D Cook, and RJ Hyndman (2020). A new tidy data structure to support │ │ exploration and modeling of temporal data, Journal of Computational and │ │ Graphical Statistics, 29:3, 466-478, doi:10.1080/10618600.2019.1695624. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/australia_tourism.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────────────────── bdg2_daily ───────────────────────────────────╮ │ Description: │ │ Daily energy consumption data from the The Building Data Genome Project 2 with │ │ building metadata and weather data. https://github.com/buds-lab/building-data- │ │ genome-project-2 │ │ │ │ Source: │ │ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome │ │ Project 2, energy meter data from the ASHRAE Great Energy Predictor III │ │ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x │ │ │ │ URL: │ │ https://drive.google.com/file/d/1KHYopzclKvS1F6Gt6GoJWKnxiuZ2aqen/view?usp=shari │ │ ng │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────────────── bdg2_daily_sample ───────────────────────────────╮ │ Description: │ │ Daily energy consumption data of two buildings sampled from the The Building │ │ Data Genome Project 2. https://github.com/buds-lab/building-data-genome- │ │ project-2 │ │ │ │ Source: │ │ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome │ │ Project 2, energy meter data from the ASHRAE Great Energy Predictor III │ │ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/refs/heads/main/data/bdg2_daily_sample.csv │ ╰─────────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────────── bdg2_hourly ───────────────────────────────────╮ │ Description: │ │ Hourly energy consumption data from the The Building Data Genome Project 2 with │ │ building metadata and weather data. https://github.com/buds-lab/building-data- │ │ genome-project-2 │ │ │ │ Source: │ │ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome │ │ Project 2, energy meter data from the ASHRAE Great Energy Predictor III │ │ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x │ │ │ │ URL: │ │ https://drive.google.com/file/d/1I2i5mZJ82Cl_SHPTaWJmLoaXnntdCgh7/view?usp=shari │ │ ng │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────── bdg2_hourly_sample ───────────────────────────────╮ │ Description: │ │ Daily energy consumption data of two buildings sampled from the The Building │ │ Data Genome Project 2. https://github.com/buds-lab/building-data-genome- │ │ project-2 │ │ │ │ Source: │ │ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome │ │ Project 2, energy meter data from the ASHRAE Great Energy Predictor III │ │ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/refs/heads/main/data/bdg2_hourly_sample.csv │ ╰─────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────────── bicimad ─────────────────────────────────────╮ │ Description: │ │ This dataset contains the daily users of the bicycle rental service (BiciMad) in │ │ the city of Madrid (Spain) from 2014-06-23 to 2022-09-30. │ │ │ │ Source: │ │ The original data was obtained from: Portal de datos abiertos del Ayuntamiento │ │ de Madrid https://datos.madrid.es/portal/site/egob │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/bicimad_users.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────── bike_sharing ──────────────────────────────────╮ │ Description: │ │ Hourly usage of the bike share system in the city of Washington D.C. during the │ │ years 2011 and 2012. In addition to the number of users per hour, information │ │ about weather conditions and holidays is available. │ │ │ │ Source: │ │ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository. │ │ https://doi.org/10.24432/C5W894. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/bike_sharing_dataset_clean.csv │ ╰─────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────── bike_sharing_extended_features ─────────────────────────╮ │ Description: │ │ Hourly usage of the bike share system in the city of Washington D.C. during the │ │ years 2011 and 2012. In addition to the number of users per hour, the dataset │ │ was enriched by introducing supplementary features. Addition includes calendar- │ │ based variables (day of the week, hour of the day, month, etc.), indicators for │ │ sunlight, incorporation of rolling temperature averages, and the creation of │ │ polynomial features generated from variable pairs. All cyclic variables are │ │ encoded using sine and cosine functions to ensure accurate representation. │ │ │ │ Source: │ │ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository. │ │ https://doi.org/10.24432/C5W894. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/bike_sharing_extended_features.csv │ ╰─────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────── ett_m1 ─────────────────────────────────────╮ │ Description: │ │ Data from an electricity transformer station was collected between July 2016 and │ │ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data │ │ points). Each data point consists of 8 features, including the date of the │ │ point, the predictive value "Oil Temperature (OT)", and 6 different types of │ │ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │ │ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL), │ │ Low UseLess Load (LULL). │ │ │ │ Source: │ │ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin & │ │ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for │ │ Long Sequence Time-Series Forecasting. │ │ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436). │ │ https://github.com/zhouhaoyi/ETDataset │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/refs/heads/main/data/ETTm1.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────── ett_m2 ─────────────────────────────────────╮ │ Description: │ │ Data from an electricity transformer station was collected between July 2016 and │ │ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data │ │ points). Each data point consists of 8 features, including the date of the │ │ point, the predictive value "Oil Temperature (OT)", and 6 different types of │ │ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │ │ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL), │ │ Low UseLess Load (LULL). │ │ │ │ Source: │ │ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin & │ │ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for │ │ Long Sequence Time-Series Forecasting. │ │ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436). │ │ https://github.com/zhouhaoyi/ETDataset │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/ETTm2.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────── ett_m2_extended ─────────────────────────────────╮ │ Description: │ │ Data from an electricity transformer station was collected between July 2016 and │ │ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data │ │ points). Each data point consists of 8 features, including the date of the │ │ point, the predictive value "Oil Temperature (OT)", and 6 different types of │ │ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │ │ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL), │ │ Low UseLess Load (LULL). Additional variables are created based on calendar │ │ information (year, month, week, day of the week, and hour). These variables have │ │ been encoded using the cyclical encoding technique (sin and cos transformations) │ │ to preserve the cyclical nature of the data. │ │ │ │ Source: │ │ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin & │ │ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for │ │ Long Sequence Time-Series Forecasting. │ │ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436). │ │ https://github.com/zhouhaoyi/ETDataset │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/ETTm2_extended.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────── expenditures_australia ─────────────────────────────╮ │ Description: │ │ Monthly expenditure on cafes, restaurants and takeaway food services in Victoria │ │ (Australia) from April 1982 up to April 2024. │ │ │ │ Source: │ │ Australian Bureau of Statistics. Catalogue No. 8501.0 │ │ https://www.abs.gov.au/statistics/industry/retail-and-wholesale-trade/retail- │ │ trade-australia/apr-2024/8501011.xlsx │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/refs/heads/main/data/expenditures_australia.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────── fuel_consumption ────────────────────────────────╮ │ Description: │ │ Monthly fuel consumption in Spain from 1969-01-01 to 2022-08-01. │ │ │ │ Source: │ │ Obtained from Corporación de Reservas Estratégicas de Productos Petrolíferos and │ │ Corporación de Derecho Público tutelada por el Ministerio para la Transición │ │ Ecológica y el Reto Demográfico. https://www.cores.es/es/estadisticas │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/consumos-combustibles-mensual.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭────────────────────────────────────── h2o ───────────────────────────────────────╮ │ Description: │ │ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health │ │ system had between 1991 and 2008. │ │ │ │ Source: │ │ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd │ │ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman │ │ /fpp3package, http://OTexts.com/fpp3. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/h2o.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────────────────── h2o_exog ────────────────────────────────────╮ │ Description: │ │ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health │ │ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are │ │ simulated. │ │ │ │ Source: │ │ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd │ │ Edition). http://pkg.robjhyndman.com/fpp3package/, │ │ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/h2o_exog.csv │ ╰─────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────── items_sales ───────────────────────╮ │ Description: │ │ Simulated time series for the sales of 3 different items. │ │ │ │ Source: │ │ Simulated data. │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/simulated_items_sales.csv │ ╰───────────────────────────────────────────────────────────╯
╭─────────────────────────────────── m4_daily ────────────────────────────────────╮ │ Description: │ │ Time series with daily frequency from the M4 competition. │ │ │ │ Source: │ │ Monash Time Series Forecasting Repository │ │ (https://zenodo.org/communities/forecasting) Godahewa, R., Bergmeir, C., Webb, │ │ G. I., Hyndman, R. J., & Montero-Manso, P. (2021). Monash Time Series │ │ Forecasting Archive. In Neural Information Processing Systems Track on Datasets │ │ and Benchmarks. Raw data, available in .tsf format, has been converted to │ │ Pandas format using the code provided by the authors in │ │ https://github.com/rakshitha123/TSForecasting/blob/master/utils/data_loader.py │ │ The category of each time series has been included in the dataset. This │ │ information has been obtained from the Kaggle competition page: │ │ https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/m4_daily.parquet │ ╰─────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────────────────── m4_hourly ───────────────────────────────────╮ │ Description: │ │ Time series with hourly frequency from the M4 competition. │ │ │ │ Source: │ │ Monash Time Series Forecasting Repository │ │ (https://zenodo.org/communities/forecasting) Godahewa, R., Bergmeir, C., Webb, │ │ G. I., Hyndman, R. J., & Montero-Manso, P. (2021). Monash Time Series │ │ Forecasting Archive. In Neural Information Processing Systems Track on Datasets │ │ and Benchmarks. Raw data, available in .tsf format, has been converted to │ │ Pandas format using the code provided by the authors in │ │ https://github.com/rakshitha123/TSForecasting/blob/master/utils/data_loader.py │ │ The category of each time series has been included in the dataset. This │ │ information has been obtained from the Kaggle competition page: │ │ https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/m4_hourly.parquet │ ╰─────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────────────────────── m5 ───────────────────────────────────────╮ │ Description: │ │ Daily sales data from the M5 competition with product metadata and calendar │ │ data. │ │ │ │ Source: │ │ Addison Howard, inversion, Spyros Makridakis, and vangelis. M5 Forecasting - │ │ Accuracy. https://kaggle.com/competitions/m5-forecasting-accuracy, 2020. Kaggle. │ │ │ │ URL: │ │ Data is stored in multiple files: https://drive.google.com/file/d/1JOqBsSHegly │ │ 6iSJFgmkugAko734c6ZW5/view?usp=sharing https://drive.google.com/file/d/1BhO1BU │ │ vs-d7ipXrm7caC3Wd_d0C_6PZ8/view?usp=sharing https://drive.google.com/file/d/1o │ │ HwkQ_QycJVTZMb6bH8C2klQB971gXXA/view?usp=sharing https://drive.google.com/file │ │ /d/1OvYzFlDG04YgTvju2k02vHEOj0nIuwei/view?usp=sharing │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────── public_transport_madrid ─────────────────────────────╮ │ Description: │ │ Daily users of public transport in Madrid (Spain) from 2023-01-01 to 2024-12-15. │ │ │ │ Source: │ │ Consorcio Regional de Transportes de Madrid CRTM, CRTM Evolucion demanda diaria │ │ https://datos.crtm.es/documents/a7210254c4514a19a51b1617cfd61f75/about │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/refs/heads/main/data/public-transport-madrid.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────── store_sales ──────────────────────────────────╮ │ Description: │ │ This dataset contains 913,000 sales transactions from 2013-01-01 to 2017-12-31 │ │ for 50 products (SKU) in 10 stores. │ │ │ │ Source: │ │ The original data was obtained from: inversion. (2018). Store Item Demand │ │ Forecasting Challenge. Kaggle. https://kaggle.com/competitions/demand- │ │ forecasting-kernels-only │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/store_sales.csv │ ╰────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────── turbine_emission ────────────────────────────────╮ │ Description: │ │ The dataset contains 36733 instances of 11 sensor measures aggregated over one │ │ hour, from a gas turbine located in Turkey for the purpose of studying flue gas │ │ emissions, namely CO and NOx. Available variables include: Ambient temperature │ │ (AT), Ambient pressure (AP), Ambient humidity (AH), Air filter difference │ │ pressure (AFDP), Gas turbine exhaust pressure (GTEP), Turbine inlet temperature │ │ (TIT), Turbine after temperature (TAT), Compressor discharge pressure (CDP), │ │ Turbine energy yield (TEY), Carbon monoxide (CO), and Nitrogen oxides (NOx). │ │ Covered period from 2011-01-01 00:00:00 to 2015-03-11 12:00:00. │ │ │ │ Source: │ │ https://archive.ics.uci.edu/dataset/551/gas+turbine+co+and+nox+emission+data+set │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/refs/heads/main/data/turbine_emission.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────── uk_daily_flights ────────────────────────────────╮ │ Description: │ │ Daily number of flights in UK from 02/01/2019 to 23/01/2022. │ │ │ │ Source: │ │ Experimental statistics published as part of the Economic activity and social │ │ change in the UK, real-time indicators release, Published 27 January 2022. Daily │ │ flight numbers are available in the dashboard provided by the European │ │ Organisation for the Safety of Air Navigation (EUROCONTROL). https://www.ons.gov │ │ .uk/economy/economicoutputandproductivity/output/bulletins/economicactivityandso │ │ cialchangeintheukrealtimeindicators/latest │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/uk_daily_flights.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────── vic_electricity ─────────────────────────────╮ │ Description: │ │ Half-hourly electricity demand for Victoria, Australia │ │ │ │ Source: │ │ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse │ │ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/, │ │ https://github.com/tidyverts/tsibbledata/. │ │ https://tsibbledata.tidyverts.org/reference/vic_elec.html │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/vic_electricity.csv │ ╰──────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────── website_visits ──────────────────────────────╮ │ Description: │ │ Daily visits to the cienciadedatos.net website registered with the google │ │ analytics service. │ │ │ │ Source: │ │ Amat Rodrigo, J. (2021). cienciadedatos.net (1.0.0). Zenodo. │ │ https://doi.org/10.5281/zenodo.10006330 │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/visitas_por_dia_web_cienciadedatos.csv │ ╰───────────────────────────────────────────────────────────────────────────╯
╭──────────────────────────────── wikipedia_visits ────────────────────────────────╮ │ Description: │ │ Log daily page views for the Wikipedia page for Peyton Manning. Scraped data │ │ using the Wikipediatrend package in R. │ │ │ │ Source: │ │ https://github.com/facebook/prophet/blob/{version}/examples/example_wp_log_peyto │ │ n_manning.csv │ │ │ │ URL: │ │ https://raw.githubusercontent.com/skforecast/skforecast- │ │ datasets/main/data/wikipedia_visits.csv │ ╰──────────────────────────────────────────────────────────────────────────────────╯