The consolidation of forecests with regression models
- Venter, Daniel Jacobus Lodewyk
- Authors: Venter, Daniel Jacobus Lodewyk
- Date: 2014
- Subjects: Regression analysis -- Mathematical models , Forecasting -- Mathematical models
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:10582 , http://hdl.handle.net/10948/d1020964
- Description: The primary objective of this study was to develop a dashboard for the consolidation of multiple forecasts utilising a range of multiple linear regression models. The term dashboard is used to describe with a single word the characteristics of the forecasts consolidation application that was developed to provide the required functionalities via a graphical user interface structured as a series of interlinked screens. Microsoft Excel© was used as the platform to develop the dashboard named ConFoRM (acronym for Consolidate Forecasts with Regression Models). The major steps of the consolidation process incorporated in ConFoRM are: 1. Input historical data. Select appropriate analysis and holdout samples. 3. Specify regression models to be considered as candidates for the final model to be used for the consolidation of forecasts. 4. Perform regression analysis and holdout analysis for each of the models specified in step 3. 5. Perform post-holdout testing to assess the performance of the model with best holdout validation results on out-of-sample data. 6. Consolidate forecasts. Two data transformations are available: the removal of growth and time-periods effect from the time series; a translation of the time series by subtracting ̅i, the mean of all the forecasts for data record i, from the variable being predicted and its related forecasts for each data record I. The pre-defined regression models available for ordinary least square linear regression models (LRM) are: a. A set of k simple LRM’s, one for each of the k forecasts; b. A multiple LRM that includes all the forecasts: c. A multiple LRM that includes all the forecasts and as many of the first-order interactions between the input forecasts as allowed by the sample size and the maximum number of predictors provided by the dashboard with the interactions included in the model to be those with the highest individual correlation with the variable being predicted; d. A multiple LRM that includes as many of the forecasts and first-order interactions between the input forecasts as allowed by the sample size and the maximum number of predictors provided by the dashboard: with the forecasts and interactions included in the model to be those with the highest individual correlation with the variable being predicted; e. A simple LRM with the predictor variable being the mean of the forecasts: f. A set of simple LRM’s with the predictor variable in each case being the weighted mean of the forecasts with different formulas for the weights Also available is an ad hoc user specified model in terms of the forecasts and the predictor variables generated by the dashboard for the pre-defined models. Provision is made in the regression analysis for both of forward entry and backward removal regression. Weighted least squares (WLS) regression can be performed optionally based on the age of forecasts with smaller weight for older forecasts.
- Full Text:
- Date Issued: 2014
- Authors: Venter, Daniel Jacobus Lodewyk
- Date: 2014
- Subjects: Regression analysis -- Mathematical models , Forecasting -- Mathematical models
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:10582 , http://hdl.handle.net/10948/d1020964
- Description: The primary objective of this study was to develop a dashboard for the consolidation of multiple forecasts utilising a range of multiple linear regression models. The term dashboard is used to describe with a single word the characteristics of the forecasts consolidation application that was developed to provide the required functionalities via a graphical user interface structured as a series of interlinked screens. Microsoft Excel© was used as the platform to develop the dashboard named ConFoRM (acronym for Consolidate Forecasts with Regression Models). The major steps of the consolidation process incorporated in ConFoRM are: 1. Input historical data. Select appropriate analysis and holdout samples. 3. Specify regression models to be considered as candidates for the final model to be used for the consolidation of forecasts. 4. Perform regression analysis and holdout analysis for each of the models specified in step 3. 5. Perform post-holdout testing to assess the performance of the model with best holdout validation results on out-of-sample data. 6. Consolidate forecasts. Two data transformations are available: the removal of growth and time-periods effect from the time series; a translation of the time series by subtracting ̅i, the mean of all the forecasts for data record i, from the variable being predicted and its related forecasts for each data record I. The pre-defined regression models available for ordinary least square linear regression models (LRM) are: a. A set of k simple LRM’s, one for each of the k forecasts; b. A multiple LRM that includes all the forecasts: c. A multiple LRM that includes all the forecasts and as many of the first-order interactions between the input forecasts as allowed by the sample size and the maximum number of predictors provided by the dashboard with the interactions included in the model to be those with the highest individual correlation with the variable being predicted; d. A multiple LRM that includes as many of the forecasts and first-order interactions between the input forecasts as allowed by the sample size and the maximum number of predictors provided by the dashboard: with the forecasts and interactions included in the model to be those with the highest individual correlation with the variable being predicted; e. A simple LRM with the predictor variable being the mean of the forecasts: f. A set of simple LRM’s with the predictor variable in each case being the weighted mean of the forecasts with different formulas for the weights Also available is an ad hoc user specified model in terms of the forecasts and the predictor variables generated by the dashboard for the pre-defined models. Provision is made in the regression analysis for both of forward entry and backward removal regression. Weighted least squares (WLS) regression can be performed optionally based on the age of forecasts with smaller weight for older forecasts.
- Full Text:
- Date Issued: 2014
Weather neutral models for short-term electricity demand forecasting
- Authors: Nyulu, Thandekile
- Date: 2013
- Subjects: Electric power consumption -- Forecasting -- Mathematical models , Forecasting -- Mathematical models
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10578 , http://hdl.handle.net/10948/d1018751
- Description: Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.
- Full Text:
- Date Issued: 2013
- Authors: Nyulu, Thandekile
- Date: 2013
- Subjects: Electric power consumption -- Forecasting -- Mathematical models , Forecasting -- Mathematical models
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10578 , http://hdl.handle.net/10948/d1018751
- Description: Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.
- Full Text:
- Date Issued: 2013
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