The Time Series Breakdown
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:
|
| 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:
|
| 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. |