Time Series Forecasting Best Practices

Tips for Effective Forecasting Questions

Be Specific Instead of asking broad questions like "How will our sales perform?", ask about a specific time frame or metric. For example, "What's the projected sales for the next quarter?".
Use Natural Language Auto understands conversational language. Frame your questions in a natural way, as if you were asking a colleague.
Include Relevant Attributes Include the necessary attributes and metrics in your question to ensure Auto understands the context.
Leverage the auto-complete feature For optimal forecasting using Auto, it's recommended to choose metrics and attributes from the auto-complete suggestions. This ensures precise understanding by Auto for accurate forecasting.
Avoid Ambiguity Keep your questions clear and unambiguous. Complex or convoluted queries might lead to inaccurate responses.
While Strategy enables forecasting of higher-level time units against lower-level data using Auto (such as forecasting next year's cost based on monthly attributes), there are key considerations:

Limit on 100 Forecast Points When requesting a forecast of a higher-level time unit against lower-level data, be aware of the 100-point forecast limit. For instance, if your dashboard data is at the daily level and you request a forecast for the next year, the forecast would encompass 1 year's worth of daily data points, exceeding the 100-point limit.
Forecast Failure for Incompatible Levels Asking for a forecast of lower-level data against higher-level data results in a forecast failure. For instance, if your dashboard data is at the monthly level and you ask for a forecast for the next week's or next day's values, it won't be feasible due to the data level discrepancy.

Ensure Sufficient Data Volume for Accurate Forecasting

For more accurate forecasting results, it's important to ensure that your data volume is substantial enough. Behind the scenes, Strategy automatically detects the seasonality of your data. To achieve optimal forecasting outcomes, we recommend that the data you intend to forecast should have at least two complete seasons of historical data.
Use High-Quality Continuous Time-Based Data for Forecasting

While Strategy performs lightweight data processing before forecasting, such as eliminating duplicated data and filling in some missing metric data, it's advisable to conduct forecasting on continuous, high-quality, time-based data. Forecasting results can be compromised if there's a significant amount of missing metric data. It's important to note that forecasts may fail or yield suboptimal outcomes if attributes contain NULL or NaN (Not-a-Number) values.

Get the most out of your forecast line chart visualization!

Hover for Insights Hover your cursor over data points on the forecast line chart to reveal tooltips. These tooltips provide detailed information about forecasted values, as well as upper and lower bounds.
Info icon Hover over the info icon to see information about the underlying model and its parameters.
Expand for Clarity If you need more detailed information and the ability to display the entire set of data points, expand the visualization. This enhances the readability of the chart.