Understand advanced attributes in forecast error metrics table

TABLE OF CONTENTS

• How do you access advanced attributes in error metrics?
• Understand Advanced attributes
  • Forecast methods
  • Standard deviation:
  • CV² (Coefficient of Variation Squared):
  • Average demand interval:
  • Non-zero demand intervals:
  • Demand classification:
  • Trend strength:
  • Seasonality strength:
  • Error

How do you access advanced attributes in error metrics?

Toggle the advanced attributes in the error metrics page; this will show additional columns in the metrics table with additional attributes.

Understand Advanced attributes

The advanced attributes include the following.

Forecast methods

When the planner selects the Best model (let the forecast engine choose) during forecast creation, the forecast engine will select the best model to calculate the forecast's statistical values.

Standard deviation:

Measures how much demand fluctuates from its average over time. A higher standard deviation indicates less predictable, more volatile demand, while a lower value suggests steadier demand. It helps planners understand the level of uncertainty in historical demand.

CV² (Coefficient of Variation Squared):

Represents demand variability normalized by the mean demand, making it easier to compare across products with different volumes. A lower CV² means a more stable and consistent demand, whereas a higher value indicates greater irregularity. It is often used in demand classification models.

Average demand interval:

Indicates the average time gap (in periods) between two consecutive non-zero demand occurrences. A higher value suggests that demand occurs less frequently, signaling intermittent or sporadic demand patterns. It helps identify slow-moving or non-continuous-demand items.

Non-zero demand intervals:

Represents the number of periods in which demand was greater than zero. A higher count indicates more regular demand, while a lower count indicates intermittent or lumpy demand. This metric helps planners assess demand continuity and forecast reliability.

Demand classification:

Categorizes demand into types such as smooth, erratic, lumpy, or intermittent based on its variability and frequency. This classification helps planners apply the proper forecasting technique for each product. It improves accuracy by aligning models with actual demand behavior.

Trend strength:

Indicates whether demand is moving up or down over time. A strong positive trend implies growing demand, while a negative trend suggests declining interest or sales. Tracking trend strength helps in adjusting forecasts and inventory plans proactively.

Seasonality strength:

Measures how strongly seasonal patterns influence demand across time periods. High seasonality strength indicates that demand is concentrated in specific cycles (e.g., holidays, harvest seasons), while low seasonality indicates that demand is relatively uniform. This helps in fine-tuning seasonal forecast adjustments.

Error

The Error column provides insights into whether a selected product has sufficient historical data. Some forecasting methods require at least six months of history, and if an item lacks enough data, this column will indicate the issue precisely for that item. This helps planners identify cases where the forecast method might not be suitable due to limited historical data.