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A grey model with opposite-direction average generating operator is put forward in order to fully extract information concealed in new data. Besides, the relationship between the sample size and the error from the inverse opposite-direction average generating operator is discussed. Compared with traditional grey forecasting model, the results of practical numerical examples have demonstrated that...
dynamic cloud workloads necessitate forecasting methodologies for accurate resource provisioning affecting both cloud providers and clients. This paper focuses on forecasting in the cloud in order to understand its underlying workload dynamics. It analyzes recent workload traces and discovers characteristics that are not adequately captured by traditional linear & nonlinear models employed for...
In the modern society, energy consumption such as gas and electricity is closely related to the weather condition because of the large share of weather-sensitive electrical appliances. Investigating how weather influences the energy consumption is of great significance for energy demand forecasting. This paper proposes an optimum regression approach for analyzing weather influence on the energy consumption...
For economic operation of distribution system with charging stations, forecasting of charging load becomes important. Based on the load data from charging posts, the first step is to distinguish whether the time sequence is stationary or non-stationary. Then with theory of time series analysis, the dynamic model is properly constructed and is used in forecasting after the model is verified to meet...
Corporate financial planning relies on thousands of financial forecasts generated by human forecasters with varying performance (forecast errors). Previous work proposes ARIMA prediction as a competitive benchmark for manual forecasts. However, ARIMA can also produce large errors, and a company needs to understand sensitivity of ARIMA-outcome to time series characteristics before ARIMA-benchmarks...
The accumulating method based on the non-equidistant sequence is proposed. Accumulated sum of each order of the non-equidistant sequence is given. Then the accumulating method can be applied to estimate parameters of the model built on the non-equidistant sequence. A new formula of the parameter estimation is proposed after introducing this method into the non-equidistant GM(1,1) model. Furthermore,...
The paper proposes a new forecasting method for mid-long term electric power load based on the fuzzy optimal theory. The proposed model considering various kinds of error information overcomes the shortcoming of single model as well as the traditional combined model whose objective is to improve just one error index. Through the case study, we prove that the accuracy can be improved obviously and...
In this paper, the importance of curvature term structure movements on fitting and forecasting of interest rates term structure is analyzed. An extension of the exponential three-factor Dynamic Nelson-Siegel model-Dynamic Four Shape Factors model is proposed based on the Shape Factors Framework, where a fourth factor captures a second type of curvature. The new factor enhances model ability to generate...
In recent years, with the sustained and rapid development of economy and a huge demand for energy consumption in rural areas, the electricity's supply can not meet the requirements of rural electricity consumption development, and turns into a bottleneck to economic development in rural areas. It became particularly important that the author took a research of electricity consumption in rural areas...
Studies of paleoclimate variations in local regions are seriously restricted by the low resolution and uncertainties of the simulated data at present. In order to apply large-scale modeling data to paleoclimate research in local regions, an effective downscaling model based on three-layer back propagation neural network (BPNN) is developed. Observational and ECHO-G simulated data are employed to train...
With the deepening reform to power industries, power systems are going to gradually open the demand side and the power consumers have to face the changes of their role in the market, and make a preliminary long-term forecasting for the electricity prices. In order to consider the relations between the spot market and the long-term contract market, this paper builds a multi-electricity price grey model...
Distribution demand forecast is one of the core of the logistics system planning. In this paper, to forecast distribution demand, a Grey Markov Model is presented by means of combining Grey system theory with dispersed Markov Chains theory. The model overcomes the influence of random fluctuation data on forecasting precision and widens the application scope of the grey forecasting. Results show that...
Introducing chaos theory into the floodwater disaster resources field, the forecasting model for the inundated area of flood disaster was brought forward integrating reconstruction of phase space and neural network. One dimension inundated area series is developed many dimension inundated area series with reconstruction of phase space, and multi- dimension series include the ergodic information, so...
The aim of this project is to develop a river water pollution predictor. We present an improved Grey-based prediction algorithm to forecast the trend of the river water pollution. We adopted grey prediction as a forecasting means because of its fast calculation with as few as four data inputs needed. However, our preliminary study shows that the general Grey model, GM (1, 1) is inadequate to handle...
This paper first analyzes the construction and trend of the energy consumption in Jiangsu. And then a non-linear forecasting model and an ARMIMA model are established based on energy consumption data from 1985 to 2007. The two methods are connected to build a combination forecasting model. Finally the prediction results of the new model of future energy consumption are analyzed. The combination model...
In order to improve the fitting and forecasting precision and solve the problem that some data have less sensitivity leading to low simulation precision of partial least-squares regression model, particle swarm optimization algorithm is adopted to optimize the partial regression coefficient, and then partial least-squares regression model based on particle swarm optimization is built. At the same...
This paper adopted the artificial neural network, and also introduced the combination forecasting theory, established non-linear combination forecasting model based on artificial neural networks for hydrological time series forecasting. Though practical case calculating, and using the relevant evaluation index to analysis predict results, the results showed that non-linear combination forecasting...
One of the key preconditions for setting down long-term transport planning and doing reasonable transport investments scientifically is forecasting volume of freight reliably. This paper builds Grey Neural Network model according to characteristics of grey model GM(1,1) and artificial neural networks (ANN) model. Grey Neural model overcomes the shortcoming of singularity forecasting model, thereby...
The paper first discussed the development trend of automobile types that are using body of road traffic public service supply. Grey forecasting method of GM(1, 1) of automobile's adaptability to road traffic public service supply was introduced. Based on the analysis of the above, the paper did empirical analysis about grey forecasting method of GM(1, 1) of automobile's adaptability to road traffic...
This paper describes the basic principle of seasonal multiplicative model in time series and the GARCH model, applies the former to forecast the monthly peak load, and then uses the latter to amend the forecasting error. Calculating the real data of a regional power grid, results show the forecasting precision and effect of the error modifying model.
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