validmind.TimeSeriesDescriptiveStatistics

TimeSeriesDescriptiveStatistics

@tags('time_series_data', 'analysis')

@tasks('regression')

defTimeSeriesDescriptiveStatistics(dataset):

Evaluates the descriptive statistics of a time series dataset to identify trends, patterns, and data quality issues.

Purpose

The purpose of the TimeSeriesDescriptiveStatistics function is to analyze an individual time series by providing a summary of key descriptive statistics. This analysis helps in understanding trends, patterns, and data quality issues within the time series dataset.

Test Mechanism

The function extracts the time series data and provides a summary of key descriptive statistics. The dataset is expected to have a datetime index, and the function will check this and raise an error if the index is not in a datetime format. For each variable (column) in the dataset, appropriate statistics, including start date, end date, min, mean, max, skewness, kurtosis, and count, are calculated.

Signs of High Risk

• If the index of the dataset is not in datetime format, it could lead to errors in time-series analysis.
• Inconsistent or missing data within the dataset might affect the analysis of trends and patterns.

Strengths

• Provides a comprehensive summary of key descriptive statistics for each variable.
• Helps identify data quality issues and understand the distribution of the data.

Limitations

• Assumes the dataset is provided as a DataFrameDataset object with a .df attribute to access the pandas DataFrame.
• Only analyzes datasets with a datetime index and will raise an error for other types of indices.
• Does not handle large datasets efficiently, and performance may degrade with very large datasets.