Quarterly Tourism In Australia

Tourism volumes between 1998 and 2016 per State, Region, and motive (luisblanche/quarterly-tourism-in-australia)   []

Acknowledgements

Data was converted from R to csv . source

Inspiration

Trying to build a python Package for Hierarchical Time Series Forecasting http://pkg.earo.me/hts/

Data summary

  • File 'tourism.csv'

    • Table ‘tourism’ consists of 23,408 data rows along six dimensions: ‘Column #1’, ‘Quarter’, ‘Region’, ‘State’, ‘Purpose’ and ‘Trips’

Size: 273.1 KB  Source: Kaggle  Last updated: 2021-09-30 23:37

Analysis completed in 1 minute 13 seconds (with remarks)
Detailed Status Information

Analysis fidelity reduced to speed things up

Issue code #210-001

To provide you with results in acceptable time, some of the most time-consuming analyses were skipped for this dataset. If you need a more comprehensive view, try telling our system to ignore some less important columns during the prioritization step or by adding '{IGNORE}' to the column names.

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Data quality assessment of this dataset

The data quality assessment assesses the quality of the input data and prioritizes mitigation steps based on analytical impact and ease of implementation

Data import summary for this dataset

The data import summary describes how Inspirient processed the submitted dataset ‘tourism’. The summary also provides insight into data quality issues, the contexts that were inferred, and if any enrichments were generated from the input data.

Analysis of top-priority dimension in this dataset

The top-priority dimension analysis for ‘Trips’ provides an in-depth analysis of the input column ‘Trips’ in dataset ‘tourism’. The report contains insights into the statistical properties of the values in the column and highlights related patterns of potential relevance.

Summary of anomalies in this dataset

The anomaly summary provides an overview of the most relevant anomalies in the submitted dataset ‘tourism’. The summary consists of four types of anomalies, i.e., business irregularities, data inconsistencies, deviations from a pattern and column outliers.

Summary of trends in this dataset

The trend analysis provides a comprehensive ranking of all time-series trends in dataset ‘tourism’. Trend strength is defined by how well the chosen best-fitting model (linear, non-linear or seasonal) fits the data.

Top insights discovered in this dataset

The top insights report presents the 30 most relevant insights automatically selected by Inspirient. The insights were chosen because they have a high dimension priority and highlight a relevant pattern.

Quick introduction to this dataset

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All insights currently in focus, ranked by relevance

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