validmind.TimeSeriesLinePlot

TimeSeriesLinePlot

@tags('time_series_data', 'visualization')

@tasks('regression')

defTimeSeriesLinePlot(dataset:validmind.vm_models.VMDataset):

Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time.

Purpose

The TimeSeriesLinePlot metric is designed to generate and analyze time series data through the creation of line plots. This assists in the initial inspection of the data by providing a visual representation of patterns, trends, seasonality, irregularity, and anomalies that may be present in the dataset over a period of time.

Test Mechanism

The mechanism for this Python class involves extracting the column names from the provided dataset and subsequently generating line plots for each column using the Plotly Python library. For every column in the dataset, a time-series line plot is created where the values are plotted against the dataset's datetime index. It is important to note that indexes that are not of datetime type will result in a ValueError.

Signs of High Risk

  • Presence of time-series data that does not have datetime indices.
  • Provided columns do not exist in the provided dataset.
  • The detection of anomalous patterns or irregularities in the time-series plots, indicating potential high model instability or probable predictive error.

Strengths

  • The visual representation of complex time series data, which simplifies understanding and helps in recognizing temporal trends, patterns, and anomalies.
  • The adaptability of the metric, which allows it to effectively work with multiple time series within the same dataset.
  • Enables the identification of anomalies and irregular patterns through visual inspection, assisting in spotting potential data or model performance problems.

Limitations

  • The effectiveness of the metric is heavily reliant on the quality and patterns of the provided time series data.
  • Exclusively a visual tool, it lacks the capability to provide quantitative measurements, making it less effective for comparing and ranking multiple models or when specific numerical diagnostics are needed.
  • The metric necessitates that the time-specific data has been transformed into a datetime index, with the data formatted correctly.
  • The metric has an inherent limitation in that it cannot extract deeper statistical insights from the time series data, which can limit its efficacy with complex data structures and phenomena.