TimeSeriesFrequency

Evaluates consistency of time series data frequency and generates a frequency plot.

Purpose: The purpose of the TimeSeriesFrequency test is to evaluate the consistency in the frequency of data points in a time-series dataset. This test inspects the intervals or duration between each data point to determine if a fixed pattern (such as daily, weekly, or monthly) exists. The identification of such patterns is crucial to time-series analysis as any irregularities could lead to erroneous results and hinder the model’s capacity for identifying trends and patterns.

Test Mechanism: Initially, the test checks if the dataframe index is in datetime format. Subsequently, it utilizes pandas’ infer_freq method to identify the frequency of each data series within the dataframe. The infer_freq method attempts to establish the frequency of a time series and returns both the frequency string and a dictionary relating these strings to their respective labels. The test compares the frequencies of all datasets. If they share a common frequency, the test passes, but it fails if they do not. Additionally, Plotly is used to create a frequency plot, offering a visual depiction of the time differences between consecutive entries in the dataframe index.

Signs of High Risk: - The test fails, indicating multiple unique frequencies within the dataset. This failure could suggest irregular intervals between observations, potentially interrupting pattern recognition or trend analysis. - The presence of missing or null frequencies could be an indication of inconsistencies in data or gaps within the data collection process.

Strengths: - This test uses a systematic approach to checking the consistency of data frequency within a time-series dataset. - It increases the model’s reliability by asserting the consistency of observations over time, an essential factor in time-series analysis. - The test generates a visual plot, providing an intuitive representation of the dataset’s frequency distribution, which caters to visual learners and aids in interpretation and explanation.

Limitations: - This test is only applicable to time-series datasets and hence not suitable for other types of datasets. - The infer_freq method might not always correctly infer frequency when faced with missing or irregular data points. - Depending on context or the model under development, mixed frequencies might sometimes be acceptable, but this test considers them a failing condition.