validmind.TimeSeriesHistogram

TimeSeriesHistogram

@tags('data_validation', 'visualization', 'time_series_data')

@tasks('regression', 'time_series_forecasting')

defTimeSeriesHistogram(dataset,nbins=30):

Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines.

Purpose

The TimeSeriesHistogram test aims to perform a histogram analysis on time-series data to assess the distribution of values within a dataset over time. This test is useful for regression tasks and can be applied to various types of data, such as internet traffic, stock prices, and weather data, providing insights into the probability distribution, skewness, and kurtosis of the dataset.

Test Mechanism

This test operates on a specific column within the dataset that must have a datetime type index. For each column in the dataset, a histogram is created using Plotly's histplot function. If the dataset includes more than one time-series, a distinct histogram is plotted for each series. Additionally, a Kernel Density Estimate (KDE) line is drawn for each histogram, visualizing the data's underlying probability distribution. The x and y-axis labels are hidden to focus solely on the data distribution.

Signs of High Risk

• The dataset lacks a column with a datetime type index.
• The specified columns do not exist within the dataset.
• High skewness or kurtosis in the data distribution, indicating potential bias.
• Presence of significant outliers in the data distribution.

Strengths

• Serves as a visual diagnostic tool for understanding data behavior and distribution trends.
• Effective for analyzing both single and multiple time-series data.
• KDE line provides a smooth estimate of the overall trend in data distribution.

Limitations

• Provides a high-level view without specific numeric measures such as skewness or kurtosis.
• The histogram loses some detail due to binning of data values.
• Cannot handle non-numeric data columns.
• Histogram shape may be sensitive to the number of bins used.