# Insights Report - Forecasting

The insights report fields vary based on the data input and the model type. This page covers an example of the forecasting model and what data it outputs. The dataset used here is an overview of energy demand in NYC over time.

A breakdown of your overall results from training the forecasting model. This shows the overall prediction accuracy on the control data ( in the case of Forecasting the most recent data is reserved) and the confidence bounds of the set.

Select 'See accuracy details' to expand the Predictive Performance deep dive. You can see the prediction line overlayed on the training data. In the case of the example dataset the curve of the data is clearly followed, higher peaks are predicted more accurately. There are three values calculated for the data:

- Accuracy - Predictions are usually within this percentage (plus or minus) of the actual outcome. Lower is better.
- Root Mean Square Error - measures how correct the predictions are on average. It is calculated by measuring how far away the predicted values are from the true values. Lower is better.
- Mean Absolute Error - is a common measure of forecast error in time series analysis. It is the mean of absolute value of the difference between predictions and actuals. This helps you understand the average size of prediction errors without considering if they are above or below actuals. Lower is better.

Accuracy Decay shows the decrease in certainty of the model over time. Any time based forecast will decrease in accuracy as you get further forward. Retraining the model as current data comes in ensures the model stays accurate going forward.

A graphical representation of the training data vs the predicted data. You can optionally show the confidence interval to see how strong the predictions are at any particular point.

Seasonality allows the user to break the data down into specific time windows to see how the value of interest(in this case demand) changes based on time of day, day of the week, month of the year, quarter etc.

This shows what factors most affect the results, in this case temperature and precipitation are the largest contributors to the data.

Last modified 1mo ago