Datasetsยถ
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.
In [1]:
Copied!
# 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.
In [2]:
Copied!
# Download data
# ==============================================================================
data = fetch_dataset(name="bike_sharing")
data.head()
# Download data
# ==============================================================================
data = fetch_dataset(name="bike_sharing")
data.head()
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ bike_sharing โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Hourly usage of the bike share system in the city of Washington D.C. during the โ โ years 2011 and 2012. In addition to the number of users per hour, information โ โ about weather conditions and holidays is available. โ โ โ โ Source: โ โ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository. โ โ https://doi.org/10.24432/C5W894. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/bike_sharing_dataset_clean.csv โ โ โ โ Shape: 17544 rows x 11 columns โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
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.
In [3]:
Copied!
# 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()
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ bike_sharing โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Hourly usage of the bike share system in the city of Washington D.C. during the โ โ years 2011 and 2012. In addition to the number of users per hour, information โ โ about weather conditions and holidays is available. โ โ โ โ Source: โ โ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository. โ โ https://doi.org/10.24432/C5W894. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/bike_sharing_dataset_clean.csv โ โ โ โ Shape: 17544 rows x 12 columns โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
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 |
In [4]:
Copied!
# 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
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ air_quality_valencia โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Hourly measures of several air chemical pollutant at Valencia city (Avd. โ โ Francia) from 2019-01-01 to 20213-12-31. Including the following variables: โ โ pm2.5 (ยตg/mยณ), CO (mg/mยณ), NO (ยตg/mยณ), NO2 (ยตg/mยณ), PM10 (ยตg/mยณ), NOx (ยตg/mยณ), โ โ O3 (ยตg/mยณ), Veloc. (m/s), Direc. (degrees), SO2 (ยตg/mยณ). โ โ โ โ Source: โ โ Red de Vigilancia y Control de la Contaminaciรณn Atmosfรฉrica, 46250047-Valรจncia - โ โ Av. Franรงa, https://mediambient.gva.es/es/web/calidad-ambiental/datos- โ โ historicos. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/air_quality_valencia.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโ air_quality_valencia_no_missing โโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Hourly measures of several air chemical pollutant at Valencia city (Avd. โ โ Francia) from 2019-01-01 to 20213-12-31. Including the following variables: โ โ pm2.5 (ยตg/mยณ), CO (mg/mยณ), NO (ยตg/mยณ), NO2 (ยตg/mยณ), PM10 (ยตg/mยณ), NOx (ยตg/mยณ), โ โ O3 (ยตg/mยณ), Veloc. (m/s), Direc. (degrees), SO2 (ยตg/mยณ). Missing values have โ โ been imputed using linear interpolation. โ โ โ โ Source: โ โ Red de Vigilancia y Control de la Contaminaciรณn Atmosfรฉrica, 46250047-Valรจncia - โ โ Av. Franรงa, https://mediambient.gva.es/es/web/calidad-ambiental/datos- โ โ historicos. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/air_quality_valencia_no_missing.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ashrae_daily โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Daily energy consumption data from the ASHRAE competition with building metadata โ โ and weather data. โ โ โ โ Source: โ โ Kaggle competition Addison Howard, Chris Balbach, Clayton Miller, Jeff Haberl, โ โ Krishnan Gowri, Sohier Dane. (2019). ASHRAE - Great Energy Predictor III. โ โ Kaggle. https://www.kaggle.com/c/ashrae-energy-prediction/overview โ โ โ โ URL: โ โ https://drive.google.com/file/d/1fMsYjfhrFLmeFjKG3jenXjDa5s984ThC/view?usp=shari โ โ ng โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ australia_tourism โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Quarterly overnight trips (in thousands) from 1998 Q1 to 2016 Q4 across โ โ Australia. The tourism regions are formed through the aggregation of Statistical โ โ Local Areas (SLAs) which are defined by the various State and Territory tourism โ โ authorities according to their research and marketing needs. โ โ โ โ Source: โ โ Wang, E, D Cook, and RJ Hyndman (2020). A new tidy data structure to support โ โ exploration and modeling of temporal data, Journal of Computational and โ โ Graphical Statistics, 29:3, 466-478, doi:10.1080/10618600.2019.1695624. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/australia_tourism.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ bdg2_daily โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Daily energy consumption data from the The Building Data Genome Project 2 with โ โ building metadata and weather data. https://github.com/buds-lab/building-data- โ โ genome-project-2 โ โ โ โ Source: โ โ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome โ โ Project 2, energy meter data from the ASHRAE Great Energy Predictor III โ โ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x โ โ โ โ URL: โ โ https://drive.google.com/file/d/1KHYopzclKvS1F6Gt6GoJWKnxiuZ2aqen/view?usp=shari โ โ ng โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ bdg2_daily_sample โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Daily energy consumption data of two buildings sampled from the The Building โ โ Data Genome Project 2. https://github.com/buds-lab/building-data-genome- โ โ project-2 โ โ โ โ Source: โ โ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome โ โ Project 2, energy meter data from the ASHRAE Great Energy Predictor III โ โ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/refs/heads/main/data/bdg2_daily_sample.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ bdg2_hourly โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Hourly energy consumption data from the The Building Data Genome Project 2 with โ โ building metadata and weather data. https://github.com/buds-lab/building-data- โ โ genome-project-2 โ โ โ โ Source: โ โ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome โ โ Project 2, energy meter data from the ASHRAE Great Energy Predictor III โ โ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x โ โ โ โ URL: โ โ https://drive.google.com/file/d/1I2i5mZJ82Cl_SHPTaWJmLoaXnntdCgh7/view?usp=shari โ โ ng โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ bdg2_hourly_sample โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Daily energy consumption data of two buildings sampled from the The Building โ โ Data Genome Project 2. https://github.com/buds-lab/building-data-genome- โ โ project-2 โ โ โ โ Source: โ โ Miller, C., Kathirgamanathan, A., Picchetti, B. et al. The Building Data Genome โ โ Project 2, energy meter data from the ASHRAE Great Energy Predictor III โ โ competition. Sci Data 7, 368 (2020). https://doi.org/10.1038/s41597-020-00712-x โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/refs/heads/main/data/bdg2_hourly_sample.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ bicimad โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ This dataset contains the daily users of the bicycle rental service (BiciMad) in โ โ the city of Madrid (Spain) from 2014-06-23 to 2022-09-30. โ โ โ โ Source: โ โ The original data was obtained from: Portal de datos abiertos del Ayuntamiento โ โ de Madrid https://datos.madrid.es/portal/site/egob โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/bicimad_users.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ bike_sharing โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Hourly usage of the bike share system in the city of Washington D.C. during the โ โ years 2011 and 2012. In addition to the number of users per hour, information โ โ about weather conditions and holidays is available. โ โ โ โ Source: โ โ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository. โ โ https://doi.org/10.24432/C5W894. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/bike_sharing_dataset_clean.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโ bike_sharing_extended_features โโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Hourly usage of the bike share system in the city of Washington D.C. during the โ โ years 2011 and 2012. In addition to the number of users per hour, the dataset โ โ was enriched by introducing supplementary features. Addition includes calendar- โ โ based variables (day of the week, hour of the day, month, etc.), indicators for โ โ sunlight, incorporation of rolling temperature averages, and the creation of โ โ polynomial features generated from variable pairs. All cyclic variables are โ โ encoded using sine and cosine functions to ensure accurate representation. โ โ โ โ Source: โ โ Fanaee-T,Hadi. (2013). Bike Sharing Dataset. UCI Machine Learning Repository. โ โ https://doi.org/10.24432/C5W894. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/bike_sharing_extended_features.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ett_m1 โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Data from an electricity transformer station was collected between July 2016 and โ โ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data โ โ points). Each data point consists of 8 features, including the date of the โ โ point, the predictive value "Oil Temperature (OT)", and 6 different types of โ โ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), โ โ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL), โ โ Low UseLess Load (LULL). โ โ โ โ Source: โ โ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin & โ โ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for โ โ Long Sequence Time-Series Forecasting. โ โ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436). โ โ https://github.com/zhouhaoyi/ETDataset โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/refs/heads/main/data/ETTm1.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ett_m2 โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Data from an electricity transformer station was collected between July 2016 and โ โ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data โ โ points). Each data point consists of 8 features, including the date of the โ โ point, the predictive value "Oil Temperature (OT)", and 6 different types of โ โ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), โ โ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL), โ โ Low UseLess Load (LULL). โ โ โ โ Source: โ โ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin & โ โ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for โ โ Long Sequence Time-Series Forecasting. โ โ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436). โ โ https://github.com/zhouhaoyi/ETDataset โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/ETTm2.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ett_m2_extended โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Data from an electricity transformer station was collected between July 2016 and โ โ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data โ โ points). Each data point consists of 8 features, including the date of the โ โ point, the predictive value "Oil Temperature (OT)", and 6 different types of โ โ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), โ โ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL), โ โ Low UseLess Load (LULL). Additional variables are created based on calendar โ โ information (year, month, week, day of the week, and hour). These variables have โ โ been encoded using the cyclical encoding technique (sin and cos transformations) โ โ to preserve the cyclical nature of the data. โ โ โ โ Source: โ โ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin & โ โ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for โ โ Long Sequence Time-Series Forecasting. โ โ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436). โ โ https://github.com/zhouhaoyi/ETDataset โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/ETTm2_extended.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ expenditures_australia โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Monthly expenditure on cafes, restaurants and takeaway food services in Victoria โ โ (Australia) from April 1982 up to April 2024. โ โ โ โ Source: โ โ Australian Bureau of Statistics. Catalogue No. 8501.0 โ โ https://www.abs.gov.au/statistics/industry/retail-and-wholesale-trade/retail- โ โ trade-australia/apr-2024/8501011.xlsx โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/refs/heads/main/data/expenditures_australia.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ fuel_consumption โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Monthly fuel consumption in Spain from 1969-01-01 to 2022-08-01. โ โ โ โ Source: โ โ Obtained from Corporaciรณn de Reservas Estratรฉgicas de Productos Petrolรญferos and โ โ Corporaciรณn de Derecho Pรบblico tutelada por el Ministerio para la Transiciรณn โ โ Ecolรณgica y el Reto Demogrรกfico. https://www.cores.es/es/estadisticas โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/consumos-combustibles-mensual.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ h2o โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health โ โ system had between 1991 and 2008. โ โ โ โ Source: โ โ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd โ โ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman โ โ /fpp3package, http://OTexts.com/fpp3. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/h2o.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ h2o_exog โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health โ โ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are โ โ simulated. โ โ โ โ Source: โ โ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd โ โ Edition). http://pkg.robjhyndman.com/fpp3package/, โ โ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/h2o_exog.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโ items_sales โโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Simulated time series for the sales of 3 different items. โ โ โ โ Source: โ โ Simulated data. โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/simulated_items_sales.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ m4_daily โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Time series with daily frequency from the M4 competition. โ โ โ โ Source: โ โ Monash Time Series Forecasting Repository โ โ (https://zenodo.org/communities/forecasting) Godahewa, R., Bergmeir, C., Webb, โ โ G. I., Hyndman, R. J., & Montero-Manso, P. (2021). Monash Time Series โ โ Forecasting Archive. In Neural Information Processing Systems Track on Datasets โ โ and Benchmarks. Raw data, available in .tsf format, has been converted to โ โ Pandas format using the code provided by the authors in โ โ https://github.com/rakshitha123/TSForecasting/blob/master/utils/data_loader.py โ โ The category of each time series has been included in the dataset. This โ โ information has been obtained from the Kaggle competition page: โ โ https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/m4_daily.parquet โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ m4_hourly โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Time series with hourly frequency from the M4 competition. โ โ โ โ Source: โ โ Monash Time Series Forecasting Repository โ โ (https://zenodo.org/communities/forecasting) Godahewa, R., Bergmeir, C., Webb, โ โ G. I., Hyndman, R. J., & Montero-Manso, P. (2021). Monash Time Series โ โ Forecasting Archive. In Neural Information Processing Systems Track on Datasets โ โ and Benchmarks. Raw data, available in .tsf format, has been converted to โ โ Pandas format using the code provided by the authors in โ โ https://github.com/rakshitha123/TSForecasting/blob/master/utils/data_loader.py โ โ The category of each time series has been included in the dataset. This โ โ information has been obtained from the Kaggle competition page: โ โ https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/m4_hourly.parquet โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ m5 โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Daily sales data from the M5 competition with product metadata and calendar โ โ data. โ โ โ โ Source: โ โ Addison Howard, inversion, Spyros Makridakis, and vangelis. M5 Forecasting - โ โ Accuracy. https://kaggle.com/competitions/m5-forecasting-accuracy, 2020. Kaggle. โ โ โ โ URL: โ โ Data is stored in multiple files: https://drive.google.com/file/d/1JOqBsSHegly โ โ 6iSJFgmkugAko734c6ZW5/view?usp=sharing https://drive.google.com/file/d/1BhO1BU โ โ vs-d7ipXrm7caC3Wd_d0C_6PZ8/view?usp=sharing https://drive.google.com/file/d/1o โ โ HwkQ_QycJVTZMb6bH8C2klQB971gXXA/view?usp=sharing https://drive.google.com/file โ โ /d/1OvYzFlDG04YgTvju2k02vHEOj0nIuwei/view?usp=sharing โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโ public_transport_madrid โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Daily users of public transport in Madrid (Spain) from 2023-01-01 to 2024-12-15. โ โ โ โ Source: โ โ Consorcio Regional de Transportes de Madrid CRTM, CRTM Evolucion demanda diaria โ โ https://datos.crtm.es/documents/a7210254c4514a19a51b1617cfd61f75/about โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/refs/heads/main/data/public-transport-madrid.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ store_sales โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ This dataset contains 913,000 sales transactions from 2013-01-01 to 2017-12-31 โ โ for 50 products (SKU) in 10 stores. โ โ โ โ Source: โ โ The original data was obtained from: inversion. (2018). Store Item Demand โ โ Forecasting Challenge. Kaggle. https://kaggle.com/competitions/demand- โ โ forecasting-kernels-only โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/store_sales.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ turbine_emission โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ The dataset contains 36733 instances of 11 sensor measures aggregated over one โ โ hour, from a gas turbine located in Turkey for the purpose of studying flue gas โ โ emissions, namely CO and NOx. Available variables include: Ambient temperature โ โ (AT), Ambient pressure (AP), Ambient humidity (AH), Air filter difference โ โ pressure (AFDP), Gas turbine exhaust pressure (GTEP), Turbine inlet temperature โ โ (TIT), Turbine after temperature (TAT), Compressor discharge pressure (CDP), โ โ Turbine energy yield (TEY), Carbon monoxide (CO), and Nitrogen oxides (NOx). โ โ Covered period from 2011-01-01 00:00:00 to 2015-03-11 12:00:00. โ โ โ โ Source: โ โ https://archive.ics.uci.edu/dataset/551/gas+turbine+co+and+nox+emission+data+set โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/refs/heads/main/data/turbine_emission.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ uk_daily_flights โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Daily number of flights in UK from 02/01/2019 to 23/01/2022. โ โ โ โ Source: โ โ Experimental statistics published as part of the Economic activity and social โ โ change in the UK, real-time indicators release, Published 27 January 2022. Daily โ โ flight numbers are available in the dashboard provided by the European โ โ Organisation for the Safety of Air Navigation (EUROCONTROL). https://www.ons.gov โ โ .uk/economy/economicoutputandproductivity/output/bulletins/economicactivityandso โ โ cialchangeintheukrealtimeindicators/latest โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/uk_daily_flights.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโ vic_electricity โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Half-hourly electricity demand for Victoria, Australia โ โ โ โ Source: โ โ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse โ โ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/, โ โ https://github.com/tidyverts/tsibbledata/. โ โ https://tsibbledata.tidyverts.org/reference/vic_elec.html โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/vic_electricity.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโ vic_electricity_classification โโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Hourly electricity demand for Victoria, Australia classified into three โ โ categories: 'low', 'medium' and 'high' according to the 20th and 80th โ โ percentiles. The dataset also includes temperature, holiday indicator and hour โ โ of the day as features. โ โ โ โ Source: โ โ O'Hara-Wild M, Hyndman R, Wang E, Godahewa R (2022).tsibbledata: Diverse โ โ Datasets for 'tsibble'. https://tsibbledata.tidyverts.org/, โ โ https://github.com/tidyverts/tsibbledata/. โ โ https://tsibbledata.tidyverts.org/reference/vic_elec.html โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/vic_electricity_classification.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ website_visits โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Daily visits to the cienciadedatos.net website registered with the google โ โ analytics service. โ โ โ โ Source: โ โ Amat Rodrigo, J. (2021). cienciadedatos.net (1.0.0). Zenodo. โ โ https://doi.org/10.5281/zenodo.10006330 โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/visitas_por_dia_web_cienciadedatos.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ wikipedia_visits โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ Description: โ โ Log daily page views for the Wikipedia page for Peyton Manning. Scraped data โ โ using the Wikipediatrend package in R. โ โ โ โ Source: โ โ https://github.com/facebook/prophet/blob/{version}/examples/example_wp_log_peyto โ โ n_manning.csv โ โ โ โ URL: โ โ https://raw.githubusercontent.com/skforecast/skforecast- โ โ datasets/main/data/wikipedia_visits.csv โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ