scholarly journals Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Chuanjin Jiang ◽  
Jing Zhang ◽  
Fugen Song
Keyword(s):  
Model Selection ◽  
Empirical Analysis ◽  
Cointegration Test ◽  
Single Model ◽  
Forecasting Accuracy ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Encompassing Test ◽  
Accuracy And Stability ◽  
Selection Of

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability.

A Multi-Dimensional Method for Nodal Load Forecasting

2014 ◽  
Vol 1070-1072 ◽  
pp. 708-717
Author(s):  
Zhi Yuan Pan ◽  
Chao Nan Liu ◽  
Jing Wang ◽  
Yong Wang
Keyword(s):  
Energy Flow ◽  
Load Forecasting ◽  
Least Square ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Load Pattern ◽  
And Control

The intelligent dispatch and control of future smart grid demands grasping of any nodal load pattern in the general great grid, therefore to realize the load forecasting of any nodal load is quite important. To solve this problem, focusing on overcoming the weakness of isolated nodal load forecasting and based on the correlation analysis, this paper proposes a multi-dimensional nodal load forecast system and corresponding method for effective prediction of any nodal load of the grid. This system includes topology partitioning of the grid energy flow according to layers and regions, basic forecasting unit composed of each layer’s total amount of load and its nodal loads, and combination forecasting for any node. The forecasting method is based on techniques including the multi-output least square support vector machine, Kalman filtering and the approximate optimal prediction. A case study shows that the multi-dimensional nodal load forecasting model helps to improve the forecasting accuracy, and has practical prospects.

Combination Forecasting Method Based on IOWA Operator and Application

2014 ◽  
Vol 945-949 ◽  
pp. 2515-2518
Author(s):  
Di Liang ◽  
Long Fei Ma ◽  
Ya Feng Hu ◽  
Shuang Wu
Keyword(s):  
Mean Value ◽  
Weighted Averaging ◽  
Forecasting Model ◽  
Predictive Values ◽  
Forecasting Accuracy ◽  
Optimal Weights ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Iowa Operator ◽  
Better Than

The combination forecasting model based on induced ordered weighted averaging IOWA operators. First, individual forecasting model that has higher forecasting accuracy is chosen as a criterion. Then, the deviation of predictive values between other models and standard model is computed. The weights are given according to the mean value size of the absolute value sum of deviation in every individual forecasting model in every period. Finally, a new forecasting model is built in accordance with the weighted error sum of squares. And genetic algorithm is used to solve the optimal weights. Verified by an example, the improved combination forecasting method is better than the original combination forecasting method based on IOWA operator. Forecasting accuracy is improved effectively.

Bi-Objective Superior Combination Method of the Post-Construction Settlement Forecasting of Subgrade

2011 ◽  
Vol 243-249 ◽  
pp. 4283-4287
Author(s):  
Tian Wen Lai ◽  
Qi Yun Zhou
Keyword(s):  
Measured Data ◽  
Combination Method ◽  
Forecasting Model ◽  
Model Parameters ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Curve Method ◽  
Index Curve

The more used forecasting method of post-construction settlement of subgrade is to analyze the measured data to determine the forecasting model parameters，and then use this model to forecast the post-construction settlement in actual projects. Currently used forecasting methods are Hoshino method, index curve method, hyperbolic method and so on, these methods have their advantages, disadvantages and applicability. To make the best use of the advantages and avoid the disadvantages, accordingly the combination forecasting method is proposed that can both comprehensive utilization the information provided in a variety of forecasting methods and also improvement of the forecasting accuracy. Then a superior combination forecasting model is established by the highest forecasting precision and the best forecasting stability. Taking the measured data of post-construction settlement of subgrade for example, comparison of fitting precision and forecasting precision of various forecasting methods , and show that the superior combination method has advantages of higher fitting precision, forecasting precision and their stability.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals Bayesian MAP model selection of chain event graphs

2011 ◽  
Vol 102 (7) ◽  
pp. 1152-1165 ◽  
Author(s):  
G. Freeman ◽  
J.Q. Smith
Keyword(s):  
Model Selection ◽  
Selection Of

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

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