Strategy ONE

Time Series Trend Analysis Best Practices

Tips for Effective Trend Analysis Questions

  • Use Natural Language Auto understands conversational language. Frame your questions in a natural way, as if you were asking a colleague.

  • Include Relevant Objects Include the necessary attributes and metrics in your question to ensure Auto understands the context.

  • Leverage the auto-complete feature For optimal trend analysis using Auto, it's recommended to choose metrics and attributes from the auto-complete suggestions. This ensures precise understanding by Auto for accurate trend analysis.

  • Avoid Ambiguity Keep your questions clear and unambiguous. Complex or convoluted queries might lead to inaccurate responses.

  • Trend Analysis Failure for Incompatible Levels Asking for trend analysis of lower-level data against higher-level data results in a trend analysis failure. For instance, if your dashboard data is at the monthly level and you ask for a trend analysis for the week's or day's values, it won't be feasible due to a data level discrepancy.

  • Statistical significance If the Auto detects a high p-value or low R-squared values, then the response contains a provision that the trend is not significant. This means the observed trend is likely due to random fluctuations in the data rather than the actual underlying pattern or model not providing a good fit for your data.

    Strategy recommends always checking the statistical properties of the trend analysis. Hover of the info icon in the top right of the visualization to access critical metrics like R, R-Squared, and p-value.

Best Practices When Using the Trend Analysis

  • Ensure Sufficient Data Volume for Accurate Trend Analysis

    For more accurate trend analysis results, it's important to ensure that your data volume is substantial enough. There needs to be at least three data points to run a trend analysis.

  • Use High-Quality Continuous Time-Based Data for Trend Analysis

    While Strategy performs lightweight data processing before trend analysis, such as filling in missing metric data, it's advisable to conduct trend analysis on continuous, high-quality time-based data. Trend analysis results can be compromised if there's a significant amount of missing metric data. It's important to note that trend analysis may fail or yield suboptimal outcomes if attributes contain NULL or NaN (Not-a-Number) values. While the algorithm fills in missing metric values, this may lead to an inaccurate trend analysis.

  • Statistical Significance

    When conducting time series linear trend analysis, it's crucial to consider the statistical significance of the observed trend. Statistical significance provides an objective measure of whether the observed trend is likely due to an actual underlying pattern or merely a result of random fluctuations in the data. Use the tooltip to access critical metrics like R, R-Squared, and p-value.

Optimize Trend Analysis Line Chart Interpretation

Get the most out of your trend analysis line chart visual!

  • Info icon Hover over the info icon to see information about the statistical properties of the underlying model and its parameters.

  • Expand for Clarity If you need more detailed information and display the entire set of data points, expand the visualization or set Container Fit to None. This enhances the readability of the chart.