The Time Series Breakdown

The Time Series Breakdown is described in detail in the Explanation tab of your report. The elements in the report can be different depending on the type of modeling technique used to build your predictive model.
Note

We describe the modeling techniques used by Smart Predict in the two tables below. In the first table, we describe the time series breakdown modeling technique that may be applied when you're working with a time series with limited disruptions. In the second table, we describe the smoothing technique that may be applied when you're working with a disrupted time series that doesn't follow a regular trend or cycle.

1. When a time series breakdown technique is used to create your predictive model, your report can contain the following information:

Information Description
Textual explanation The textual information describes the modeling technique that is used to calculate the forecast. This is textual explanation you see in the report:

The predictive model was built by breaking down the time series into different components.

Actual The Actual is the observed historical data.
Trend The Trend is the general orientation of the time series. The report can show linear or piece-wise trends.
Cycles A time series predictive scenario can detect fixed length or seasonal cycles. Fixed length cycles recur every N observations. 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

Influencers These represent the part of the time series impacted by the influencers specified in the field Influencers of the predictive model settings.
Fluctuations Fluctuations represent the part of the time series detected by the predictive model that is dependent on past values of the time series. The report shows the influence of the last observations before the predictive forecast. Fluctuations reflect changes that are not detected at the trend and cycle level.

For example, the predictive model can detect that the previous 2 values have an impact on the actual values.

For more information you can refer to the chapter called Past Target Value Contributions.

Residuals Residuals refer to what is left when the trend, cycles, and fluctuations have been extracted from the initial time series. Residuals are neither systematic nor predictable. They reflect the part of the time series that Smart Predict can't explain or model. The smaller the residuals, the better the predictive model. A good predictive model produces residual data that contains no pattern.

2. When a time series smoothing technique is used to create your predictive model, your report can contain the following information:

Information Description
Textual explanation The textual explanation describes the modeling technique that is used to calculate the forecast. This is textual explanation you see in the report:

The predictive model was built incrementally by smoothing the time series, with more weight given to recent observations.

Actual The Actual is the observed historical data.
Forecast The Forecast is the result of the prediction in the future.
Trend The Trend is the orientation of the forecast data. It is calculated using an algorithm that applies an exponential smoothing on the past data over time.
Cycles
A time series predictive scenario can detect seasonal cycles, with or without amplitude variations. These cycles are calculated using an algorithm that applies an exponential smoothing techniques on the past data over time. The recurrence of seasonal cycles is based on a calendar time unit such as day, week, month etc. The report shows the recurrence of the cyclic pattern. The following seasonal cycles can be detected:
  • a pattern recurring every semester

  • a pattern recurring every quarter

  • a pattern recurring every month

  • a pattern recurring every two weeks

  • a pattern recurring every week

  • a pattern recurring every day

  • a pattern recurring every hour

  • a pattern recurring every minute

  • a pattern recurring every second

Residuals Residuals refer to what is left when the trend and cycles have been extracted from the initial time series. Residuals are neither systematic nor predictable. The smaller the residuals, the better the predictive model. A good predictive model produces residual data that contains no pattern.
Note
If you choose to get predictive forecasts per entity, you have this information for each entity.