The Signal Decomposition

You can analyze the signal by checking the following information:

Information	What's this?
Signal	The target (the observed data)
Trend	General orientation of the signal. The following types of trend are indicated: Linear, and Quadratic when the signal rises and falls inconsistantly. Note In certain cases, a data smoothing technique is applied to the results to help reduce volatility or noise in the data. In this case, the trend and cycle analyses are unified as a trend, with the information given that a data smoothing technique was used.
Cycles	Smart Predict can detect fixed length or seasonal cycles. Fixed length cycles recur every N observation. The recurrence of seasonal cycles is based on a calendar time unit such as day, week, month etc. For seasonal cycles, the report shows the recurrence of the cyclic pattern as well as the time granularity that makes the cyclic pattern appear. The following seasonal cycles can be detected: a pattern recurring every year when observed on a half monthly basis a pattern recurring every year when observed on a monthly basis a pattern recurring every year when observed on a semester basis a pattern recurring every year when observed on a weekly basis a pattern recurring every quarter when observed on a monthly basis a pattern recurring every semester when observed on a monthly basis a pattern recurring every month when observed on a weekly basis a pattern recurring every year when observed on a daily basis a pattern recurring every month when observed on a daily basis a pattern recurring every week when observed on a daily basis a pattern recurring every hour when observed on a minute basis a pattern recurring every day when observed on an hourly basis a pattern recurring every minute when observed on a second basis
Fluctuation	Part of the signal detected by the predictive model that is completely dependent on past values of the signal up to a certain point in time. The influence of the last observations before the predictive forecast is indicated for the signal.
Residuals	What is left when the trend, the cycles, and the fluctuation have been extracted from the signal. It results from short term fluctuations in the series, which are neither systematic nor predictable: it's the part of the signal that the predictive model can't explain or model. The smaller the residuals, the better the predictive model. A good predictive model produces residual data that contain no pattern.
Outliers	An actual signal value is qualified as outlier once its corresponding forecasting error is considered to be abnormal relative to the forecasting error mean observed on the estimation data source. The forecasting error indicator is the absolute difference between the actual and predicted values. This is also called the residue. The residue abnormal threshold is set to 3 times the standard deviation of the residue values on an estimation (or validation) data source. See The Signal Outliers for more information.

Note

If you have chosen to get predictive forecasts per entity, you have this information for each entity.