# Datasets:monash_tsf

Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: no-annotation
Source Datasets: original
Dataset Preview
start (unknown)target (sequence)feat_static_cat (sequence)feat_dynamic_real (sequence)item_id (string)
"2002-01-01T00:00:00"
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"T1"
"2002-01-01T00:00:00"
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"T2"
"2002-01-01T00:00:00"
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"T3"
"2002-01-01T00:00:00"
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"T4"
"2002-01-01T00:00:00"
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[ 4 ]
"T5"

# 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.

#### 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.

## 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.

## Considerations for Using the Data

### 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.