validmind.test_suites.time_series

Test suite for data quality on time series datasets

This test suite provides a preliminary understanding of the target variable(s) used in the time series dataset. It visualizations that present the raw time series data and a histogram of the target variable(s).

The raw time series data provides a visual inspection of the target variable's behavior over time. This helps to identify any patterns or trends in the data, as well as any potential outliers or anomalies. The histogram of the target variable displays the distribution of values, providing insight into the range and frequency of values observed in the data.

This test suite provides a preliminary understanding of the features and relationship in multivariate dataset. It presents various multivariate visualizations that can help identify patterns, trends, and relationships between pairs of variables. The visualizations are designed to explore the relationships between multiple features simultaneously. They allow you to quickly identify any patterns or trends in the data, as well as any potential outliers or anomalies. The individual feature distribution can also be explored to provide insight into the range and frequency of values observed in the data. This multivariate analysis test suite aims to provide an overview of the data structure and guide further exploration and modeling.

This test suite computes predictions from statsmodels OLS linear regression models against a list of models and plots the historical data alongside the forecasted data. The purpose of this test suite is to evaluate the performance of each model in predicting future values of a time series based on historical data. By comparing the historical values with the forecasted values, users can visually assess the accuracy of each model and determine which one best fits the data. In addition, this test suite can help users identify any discrepancies between the models and the actual data, allowing for potential improvements in model selection and parameter tuning.

This test suite performs sensitivity analysis on a statsmodels OLS linear regression model by applying distinct shocks to each input variable individually and then computing the model's predictions. The aim of this test suite is to investigate the model's responsiveness to variations in its inputs. By juxtaposing the model's predictions under baseline and shocked conditions, users can visually evaluate the sensitivity of the model to changes in each variable. This kind of analysis can also shed light on potential model limitations, including over-reliance on specific variables or insufficient responsiveness to changes in inputs. As a result, this test suite can provide insights that may be beneficial for refining the model structure, improving its robustness, and ensuring a more reliable prediction performance.

Test suite for time series datasets.

Test suite for time series model validation.