MicroStrategy ONE

Time Series Trend Analysis

Time series trend analysis is supported in MicroStrategy Cloud environments and starting in MicroStrategy ONE (March 2024), time series trend analysis is supported on the MicroStrategy Cloud for Government platform. You can only use this feature in MicroStrategy Library (web browser only) and Workstation when you connect to a Library server.

MicroStrategy ONE Update 11 introduces time series linear trend analysis. Time series linear trend analysis is a robust method enabling organizations to recognize clear upward or downward trajectories in their historical data. At its essence, it focuses on data points charted over consecutive time intervals, with a prime objective of detecting consistent linear tendencies without the distractions of short-term fluctuations. This form of analysis stands out for its precision in mapping out long-term developments and guiding strategic planning. By leveraging sophisticated statistical techniques, MicroStrategy's linear trend analysis provides a streamlined and user-friendly interface to detect and understand persistent trends in datasets. Whether you are data scientist, head of operations, or sales leader, this tool presents a platform to understand market trajectories, make proactive adjustments, and optimize long-term goals. This analytic refinement replaces traditional, often tedious methods, ensuring that users save significant time while drawing insights more effectively.

Access the Time Series Trend Analysis Feature

Time series linear trend analysis is part of the MicroStrategy AI add-on bundle and is available for MicroStrategy Cloud Environment (MCE) customers starting in MicroStrategy ONE Update 11 (September 2023). Only users and user groups with the Use Auto Assistant and ML Visualizations privilege can access the Linear Trend Analysis line chart.

Linear trend analysis is available in the following ways:

  • Through the visualization gallery in a dashboard, using a drag and drop interface to create a Linear Trend Analysis Line Chart visualization.

  • Auto can utilize natural language queries to perform trend analysis.

Create Trend Analysis Line Charts

  1. Open a dashboard for editing.

  2. In the top toolbar, click Visualization .

  3. Choose Insight+ > Linear Trend Line Chart .

  4. Drag a metric and time attribute from the Datasets panel to the Editor panel.

  5. View the rendered visualization. You can scroll to the right to view all provided values, if necessary.

Customize the Appearance of Linear Trend Analysis Line Charts

Make your trend visually stand out by customizing the appearance of your Trend Line Chart. Adjust colors, fonts, and labels to match your preferences and create a chart that's not only insightful but also looks great.

By default, the trend line takes the first color from the chosen color palette. However, users can customize the color, data labels, markers, and divider line of the trend line chart on the Format panel.

Customize Trend Lines and Lines

  1. In the Format panel, click Text and Form .

  2. Select Trend line from the drop down.

  3. Choose a trend line style and color.

  4. Select Data Labels and Shape from the drop down.

  5. Under Shape, select a line style and color.

Visualization Container Fit

The Time Series Trend visualization appears in compact mode by default. All data is condensed so that it appears within the visualization container from end to end. If you want to see more granular details, change to Container Fit: None.

  1. In the Format panel, click Visualization Options .

  2. In the Container Fit drop down, select an option. None allows you to scroll horizontally to view your data points in more detail.

Use Auto Answers for Linear Trend Analysis

To get insights from linear trend analysis using Auto Answers, simply phrase your trend inquiry in everyday language. For instance, ask "What is the trend for Profit in the last year?" Auto Answers delivers a precise trend analysis based on your question, its comprehensive grasp of your dataset, and refined statistical methods.

Auto Answer's response consists of a clear natural language description of the observed linear trend alongside a trend line chart visualization, ensuring you have a clear perspective on the historical trend path. The visualization is rendered to fit all data points from the input range into compact . This allows you to quickly glance through your data and better understand its relation to the trend line. To dive deeper into the result, the visualization's expanded mode found in top right corner.

Here's how you can delve into predictions using Auto Answers. Auto Answers is in expanded mode for the following examples:

  • Last Few Months Trend Start with a given monthly sales dataset. You can ask Auto Answers to plot the sales trend for the last few months. The algorithm adeptly handles the task even if you're requesting a trend for an entire year based on monthly data.

    Ask the question, "How was our Profit trending over past 6 months?"

  • Trend Over Periods Auto Answers can also handle trend generation over time durations. Use historical monthly data to plot trend for past events.

    Ask the question, "What was the trend for Units Sold by Month in 2022?"

  • Applying Filters Trend Analysis can also be filtered. The example below demonstrates trend analysis for the last three months, considering a specific category like music. Keep in mind that the month attribute is represented as an integer, not a time-based value.

    Ask the question, "What was the trend for Units Sold for Category music in the last three Month?"

Tips and 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.

    MicroStrategy 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 MicroStrategy 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.

Understand Trend Analysis Limitations

The limitations addressed below are associated with the MicroStrategy ONE Update 11 release. MicroStrategy is actively working on enhancing this feature, and some of these limitations may be addressed in upcoming versions. For the latest information and updates, we encourage you to visit this page periodically. By understanding and considering these limitations, you can make the most of the trend analysis feature and achieve meaningful insights.

  • Attribute and Metric Requirements For accurate predictions, make sure to use exactly one attribute and one metric in your questions to Auto. Queries not meeting this requirement might not yield optimal trends.

  • Consider Attribute Forms The attribute used for trend analysis should have at least one form with a type of date, datetime, or integer. Timestamp data is ot supported.

  • Filtering DateTime attributes Filters on dates are not supported if the attribute a DateTime type.

  • Smart Attributes Trend analysis with smart attributes is not currently supported.

  • Match data type between database and Metadata Trend analysis on data that is stored as an Integer in a data warehouse, but defined as a Date attribute in metadata is not supported.

  • Consolidation and Grouping Trend analysis against consolidated or grouped attribute elements is not possible at this time.

  • Cartesian join Trend analysis is not performed when a cartesian join is detected in the dataset(s).

  • Additional information about trend Follow-up questions about the underlying trend algorithm in Auto Answers are not supported. This includes questions such as, "What is the intercept value?" To get more information about the statistical properties of the trend analysis, use the tooltip for visualizations the info icon in Auto Answers.

  • Derived objects Trend analysis cannot be performed by Auto Answers if the requested metric is not present in an underlying dataset and needs to be created as a derived metric on the fly.

Trend Analysis - FAQs

What time series trend analysis method is used in the current release

The time series trend analysis method used in the latest release is called linear regression. This method is described on the OTexts web site. You can refer to the statsmodel Python module (Time Series analysis tsa - statsmodels 0.14.0 ) for more technical details.

What types of attributes and metrics are required for successful trend analysis?

To successfully plot trend for your data, you need either:

  • Single attribute of type date or datetime + single metric of interest (recommended)

  • Single attribute of type int + metric of interest (when date/datetime attribute is unavailable for time series trend analysis)

Is missing data handled in trend analysis?

Yes, the algorithm can automatically fill in missing values for a metric if it has corresponding time-series data. This is only done for the calculation, so the result visualization will still show missing data. The algorithm uses forward and backward fill, using the neighboring value, depending on the direction. The primary method is forward grab, which takes the previous neighbor. If the first value is missing, backward fill is run to estimate a value.

What time-based attribute trend analysis levels are supported?

Time-based attribute trend analysis is supported for Date, DateTime, or integer-based time attributes. As long as an attribute is one of the above types, the algorithm supports all time levels.