Parameter | Value | Opt. | Description | Example |
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Duration of the season | System.String - 1 year
- 52 weeks
- 1 week
- 3 months
- 1 month
- 1 day
- 12 hours
- 8 hours
- 6 hours
- 1 hour
| - | Please enter the duration of the season on which your data is based. | - |
@BOXCOXTRANSFORMATION | System.String - Automatic
- 1 (identity)
- 0.5 (root)
- 0 (logarithm)
- -1 (reciprocal value)
| - | The BOX-COX transformation converts the observations into a form that can be used for the regression analysis. If you select 'automatic' as the type of BOX-COX transformation, a suitable transformation for the data on which the analysis is based is defined. Another type of transformation should then only be applied if you have sound knowledge of the data to be analysed. Select '1 (identity)', if the seasonal variations remain absolutely constant, e.g. the December value is always 1000 units higher than the annual average. Select '0 (logarithm)', if the seasonal variations are a constant percentage, e.g. the December value is always 20% higher than the annual average. Select '0.5 (root)', if the seasonal variations are a constant percentage, but the percentage rate reduces slightly over time, e.g. the first December value is 20% higher than the monthly average, the second December value is 19% higher, etc. Select '-1 (reciprocal)' for non-negative data with falling trend, for example, as for the sales data from a book shop, where high values are observed during the first few months after publications, but then fall with time. | - |
Forecast period | System.Int32 | - | Please enter the period, e.g. the next 6 weeks, for which a forecast is to be calculated. | - |
Time Unit | System.String - Year(s)
- Month(s)
- Week(s)
- Day(s)
- Hour(s)
| - | Please enter the period, e.g. the next 6 weeks, for which a forecast is to be calculated. | - |
Deliver as result | System.String - @Forecast
- Forecast + History
- Forecast + hist. forecast
- Forecast + history + hist. forecast + confidence interval
- Only parameter estimation
- Statistics only
| - | Please select which data should be displayed in the results. | - |
Validate model over | System.String - Last 1/4 forecast period
- Last 1/2 forecast period
- Last forecast period
- Last 2 forecast periods
- Last 3 forecast periods
- Each forecast period
| - | The observations are divided into a validation period and training period, e.g. validation period = the last 52 weeks of the data on which the model is based, training period = all other data. A regression model is created on the basis of the training period, and this model is used to estimate the data of the training and validation period. The percentage and average errors between the estimated and actual observations are determined for the training and validation period. If the errors for the training and validation period are very different, your regression model contains too many variables and returns inaccurate forecasts (overfitting). In this case, please try to simplify your model by removing one or more variables. | - |
Adjusted R² | System.Double | - | The adjusted determinacy, adj. R², provides information about the quality of a regression model, i.e. how well the regression model explains the data on which it is based. A value adj. R² = 0.95 means that 95% of the fluctuations in the data can be explained by the regression. Please enter in this field a minimum limit for the adj. R² of a regression model. If the adj. R² of a model exceeds this limit, the regression model is discarded and the forecast is created using the mean value of the data. | - |
ANOVA p-value | System.Double | - | Apart from the adjusted R², the p values of the ANOVA is another indicator of the quality of a regression model. High p values (e.g. larger than 0.1) indicate that the regression poorly explains the data on which it is based. Please enter an upper limit for the p value of the ANOVA. If this value is exceeded, the model is discarded and the forecasts are estimated using the mean value of the data. | - |
p values (influencing factors) | System.Double | - | The p value of an independent variable provides information about whether the observations on which the data is based depends on any of these variables or not. Low values (e.g. less than 0.05) indicate a relationship between independent and dependent variables, high values on the other hand indicate that there are no relationships whatsoever. In this field, please enter a limit for the p value of a single independent variable. If the p value exceeds this limit, this variable will automatically be excluded from the regression model, provided you have activated 'excluded variables'. | - |
Include seasonal variations | System.String | - | The regression model tries to identify seasonal variations and to take these into account for the forecast. | - |
Include intercept | System.Boolean | - | Please do not close the point of intersection unless you are really sure that it does not play any role in your observations. | - |
Linear trend | System.Boolean | - | The regression model contains a linear trend, i.e. the observed values rise/fall linearly along the time axis. | - |
Include quadratic trend | System.Boolean | - | The regression model contains a quadratic trend, i.e. the observed values rise/fall quadratically along the time axis. | - |
Include logarithmic trend | System.Boolean | - | The regression model contains logarithmic trend, i.e. the observed values rise/fall logarithmically along the time axis. | - |
Include day in year | System.Boolean | - | The location of a date within the year is taken into account in the regression model, e.g. 3.1.2010 is the third day of the year. | - |
Include day in month | System.Boolean | - | The location of a date within the month is taken into account in the regression model , e.g. 3.1.2010 is the third day in January. | - |
Include day in week | System.Boolean | - | The effect of different week days is taken into account in the regression model. | - |
Include quarters | System.Boolean | - | Quarterly periods are taken into account in the regression model. | - |
Summer time | System.Boolean | - | Summer time is taken into account in the regression model. | - |
Include previous time interval | System.Boolean | - | The regression analysis examines, whether observations in your previous observations are affected in the scale. | - |
Include previous day | System.Boolean | - | The regression analysis examines whether observations are affected by the observations on the previous day. | - |
Previous week | System.Boolean | - | The regression analysis examines whether observations are affected by the observations in the previous week. | - |
Previous year | System.Boolean | - | The regression analysis examines whether observations are affected by the observations in the previous year. | - |
Exclude insignificant influencing factors | System.Boolean | - | Influencing factors whose p values exceed the limit given in the 'p values field (influencing factors)' will be excluded from the regression model. | - |
Ignore 0 values | System.Boolean | - | Rows with 0 values are ignored and are treated like missing data. | - |
Output the validation result in data nodes | System.Boolean | - | If selected, the validation results are displayed in a separate data node. | - |
Output error messages/warnings in data nodes | System.Boolean | - | If selected, error messages and warnings are displayed in a separate data node. | - |