--- annotations_creators: - no-annotation language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Monash Time Series Forecasting Repository size_categories: - 1K 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: ```python { '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](mailto:rakshitha.godahewa@monash.edu) * [Christoph Bergmeir](mailto:christoph.bergmeir@monash.edu) * [Geoff Webb](mailto:geoff.webb@monash.edu) * [Rob Hyndman](mailto:rob.hyndman@monash.edu) * [Pablo Montero-Manso](mailto:pablo.monteromanso@sydney.edu.au) ### Licensing Information [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) ### Citation Information ```tex @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](https://github.com/kashif) for adding this dataset.