updating dataset structure to increase usability

README.md

@@ -14,40 +14,34 @@ task_ids:
 - multivariate-time-series-forecasting
 ---
 
-# Tourism Monthly Dataset with Economic Covariates
+# 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. It has been enhanced with economic covariates, namely Consumer Price Index (CPI), Inflation Rate, and Gross Domestic Product (GDP), from official Australian government sources. It is designed for forecasting and analytical purposes in the context of tourism and its economic impacts.
+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 Description
+## Dataset Transformation and Structure
 
-The dataset spans from 1998, covering various aspects of tourism, including different types of visits (e.g., holiday, business, other), across multiple series, totaling 1311 distinct series. It includes monthly data, aiming to predict future tourism trends with a horizon of 24 months. The data has been enriched with economic indicators to provide a comprehensive analysis platform.
+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:
 
-### Series Naming Convention
+- **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.
 
-Each series name in the dataset is composed of four parts:
+### Columns Overview After Transformation:
 
-1. **The first three characters**: These represent encoded names for States, Zones, and Regions within Australia. This encoding allows users to easily identify the geographic focus of each series.
-2. **The last three letters**: These indicate the purpose of the visit, which can be for business (`Bus`), holiday (`Hol`), visiting friends or relatives (`Vis`), or other purposes (`Oth`).
+- **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.
 
-### Columns Overview:
+## Enhanced Dataset Description
 
-- **Year**: The year of the record.
-- **Month**: The month of the record.
-- **Date**: The full date of the record.
-- **Series Names** (e.g., AAAHol, AAAVis, AAABus, ...): These columns represent different tourism series, capturing various types of visits and activities. The naming convention provides insights into the geographic area and the purpose of the visits.
-- **CPI**: The Consumer Price Index, reflecting the change in the price level of a basket of consumer goods and services purchased by households.
-- **Inflation_Rate**: The rate at which the general level of prices for goods and services is rising, and, subsequently, purchasing power is falling.
-- **GDP**: Gross Domestic Product, representing the total monetary or market value of all the finished goods and services produced within Australia's borders in a specific time period.
-
-### Data Sources:
-
-- **Tourism Data**: Derived from the work of Athanasopoulos et al., 2011, focusing on the tourism sector.
-- **Economic Covariates**: Sourced from official Australian government publications, providing key economic indicators.
+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
 
-This dataset can be utilized for time-series forecasting, economic impact studies, and comprehensive analysis of the tourism industry in relation to economic trends. It's particularly useful for researchers, economists, and policymakers interested in understanding and predicting the dynamics of tourism and its interactions with the economy.
+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. Users are free to share and adapt the material for any purpose, even commercially, under the condition that appropriate credit is given, a link to the license is provided, and it is indicated if changes were made.
+This dataset is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.