Dataset Card for Monash Time Series Forecasting Repository

Dataset Summary

The first comprehensive time series forecasting repository containing datasets of related time series to facilitate the evaluation of global forecasting models. All datasets are intended to use only for research purpose. Our repository contains 30 datasets including both publicly available time series datasets (in different formats) and datasets curated by us. Many datasets have different versions based on the frequency and the inclusion of missing values, making the total number of dataset variations to 58. Furthermore, it includes both real-world and competition time series datasets covering varied domains.

The following table shows a list of datasets available:

Name	Domain	No. of series	Freq.	Pred. Len.	Source
weather	Nature	3010	1D	30	Sparks et al., 2020
tourism_yearly	Tourism	1311	1Y	4	Athanasopoulos et al., 2011
tourism_quarterly	Tourism	1311	1Q-JAN	8	Athanasopoulos et al., 2011
tourism_monthly	Tourism	1311	1M	24	Athanasopoulos et al., 2011
cif_2016	Banking	72	1M	12	Stepnicka and Burda, 2017
london_smart_meters	Energy	5560	30T	60	Jean-Michel, 2019
australian_electricity_demand	Energy	5	30T	60	Godahewa et al. 2021
wind_farms_minutely	Energy	339	1T	60	Godahewa et al. 2021
bitcoin	Economic	18	1D	30	Godahewa et al. 2021
pedestrian_counts	Transport	66	1H	48	City of Melbourne, 2020
vehicle_trips	Transport	329	1D	30	fivethirtyeight, 2015
kdd_cup_2018	Nature	270	1H	48	KDD Cup, 2018
nn5_daily	Banking	111	1D	56	Ben Taieb et al., 2012
nn5_weekly	Banking	111	1W-MON	8	Ben Taieb et al., 2012
kaggle_web_traffic	Web	145063	1D	59	Google, 2017
kaggle_web_traffic_weekly	Web	145063	1W-WED	8	Google, 2017
solar_10_minutes	Energy	137	10T	60	Solar, 2020
solar_weekly	Energy	137	1W-SUN	5	Solar, 2020
car_parts	Sales	2674	1M	12	Hyndman, 2015
fred_md	Economic	107	1M	12	McCracken and Ng, 2016
traffic_hourly	Transport	862	1H	48	Caltrans, 2020
traffic_weekly	Transport	862	1W-WED	8	Caltrans, 2020
hospital	Health	767	1M	12	Hyndman, 2015
covid_deaths	Health	266	1D	30	Johns Hopkins University, 2020
sunspot	Nature	1	1D	30	Sunspot, 2015
saugeenday	Nature	1	1D	30	McLeod and Gweon, 2013
us_births	Health	1	1D	30	Pruim et al., 2020
solar_4_seconds	Energy	1	4S	60	Godahewa et al. 2021
wind_4_seconds	Energy	1	4S	60	Godahewa et al. 2021
rideshare	Transport	2304	1H	48	Godahewa et al. 2021
oikolab_weather	Nature	8	1H	48	Oikolab
temperature_rain	Nature	32072	1D	30	Godahewa et al. 2021

Dataset Usage

To load a particular dataset just specify its name from the table above e.g.:

load_dataset("monash_tsf", "nn5_daily")

Notes:

• Data might contain missing values as in the original datasets.
• The prediction length is either specified in the dataset or a default value depending on the frequency is used as in the original repository benchmark.

Supported Tasks and Leaderboards

time-series-forecasting univariate-time-series-forecasting

The univariate time series forecasting tasks involves learning the future one dimensional target values of a time series in a dataset for some prediction_length time steps. The performance of the forecast models can then be validated via the ground truth in the validation split and tested via the test split.

multivariate-time-series-forecasting

The multivariate time series forecasting task involves learning the future vector of target values of a time series in a dataset for some prediction_length time steps. Similar to the univariate setting the performance of a multivariate model can be validated via the ground truth in the validation split and tested via the test split.

Languages

Dataset Structure

Data Instances

A sample from the training set is provided below:

{
  'start': datetime.datetime(2012, 1, 1, 0, 0),
  'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, ...],
  'feat_static_cat': [0], 
  'feat_dynamic_real': [[0.3, 0.4], [0.1, 0.6], ...],
  'item_id': '0'
}

Data Fields

For the univariate regular time series each series has the following keys:

• start : a datetime of the first entry of each time series in the dataset
• target : an array[float32] of the actual target values
• feat_static_cat : an array[uint64] which contains a categorical identifier of each time series in the dataset
• feat_dynamic_real : optional array of covariate features
• item_id : a string identifier of each time series in a dataset for reference

For the multivariate time series the target is a vector of the multivariate dimension for each time point.

Data Splits

The datasets are split in time depending on the prediction length specified in the datasets. In particular for each time series in a dataset there is a prediction length window of the future in the validation split and another prediction length more in the test split.

Dataset Creation

Curation Rationale

To facilitate the evaluation of global forecasting models. All datasets in our repository are intended for research purposes and to evaluate the performance of new forecasting algorithms.

Source Data

Initial Data Collection and Normalization

Out of the 30 datasets, 23 were already publicly available in different platforms with different data formats. The original sources of all datasets are mentioned in the datasets table above.

After extracting and curating these datasets, we analysed them individually to identify the datasets containing series with different frequencies and missing observations. Nine datasets contain time series belonging to different frequencies and the archive contains a separate dataset per each frequency.

Who are the source language producers?

The data comes from the datasets listed in the table above.

Annotations

Annotation process

The annotations come from the datasets listed in the table above.

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

• Rakshitha Godahewa
• Christoph Bergmeir
• Geoff Webb
• Rob Hyndman
• Pablo Montero-Manso

Licensing Information

Creative Commons Attribution 4.0 International

Citation Information

@InProceedings{godahewa2021monash,
    author = "Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoffrey I. and Hyndman, Rob J. and Montero-Manso, Pablo",
    title = "Monash Time Series Forecasting Archive",
    booktitle = "Neural Information Processing Systems Track on Datasets and Benchmarks",
    year = "2021",
    note = "forthcoming"
}

Contributions

Thanks to @kashif for adding this dataset.