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time_series_datasets

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language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100k
task_categories:
  - time-series-forecasting
task_ids:
  - univariate-time-series-forecasting
  - multivariate-time-series-forecasting

Tourism Monthly Time Series Dataset with Economic and Static Covariates

This dataset, originally sourced from Athanasopoulos et al. (2011), focuses on the tourism industry with a monthly frequency and has been enhanced with economic covariates (e.g., CPI, Inflation Rate, GDP) from official Australian government sources. We also perform some preprocessing to further increase the usability of the dataset with dynamic start dates for each series and static covariates for in-depth time series forecasting and analysis in the context of tourism and its economic impacts.

Dataset Transformation and Structure

The dataset has undergone a preprocessing transformation to optimize it for time series analysis, specifically to enhance its utility for forecasting tasks. This preprocessing includes:

  • Unique ID Creation: A unique identifier is assigned to each series, facilitating the analysis of individual time series within the broader dataset.
  • Dates: The start date for each series is dynamically set based on the first date where the target variable (visits) is non-zero.
  • Static Covariates: Four static covariates are extracted based on the type of tourism, enriching the dataset with additional dimensions for analysis.

Columns Overview After Transformation:

  • Unique ID: A combination of encoded names for States, Zones, Regions within Australia, and the purpose of the visit (e.g., business, holiday, visiting, other).
  • Time Column: Represents the time dimension of the dataset, dynamically adjusted for each series.
  • Target: The target variable for forecasting, specifically focusing on visits.
  • Dynamic Covariates: Economic indicators such as CPI, Inflation Rate, and GDP that vary over time.
  • Static Covariates (Static_1 to Static_4): Extracted from the unique ID, these provide additional information for analysis, including geographic and purpose-of-visit details.

Enhanced Dataset Description

This enriched dataset not only includes monthly data on various aspects of tourism but also incorporates dynamic economic indicators and static covariates derived from preprocessing. This structure is particularly useful for advanced time series forecasting models that can leverage both dynamic changes over time and static attributes of the series.

Usage

The transformed dataset is intended for researchers, economists, and policymakers for forecasting tourism trends, understanding the economic impact of tourism, and conducting comprehensive analysis leveraging both temporal dynamics and static characteristics.

License

This dataset is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.