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This paper proposes a data-driven modeling method for building energy consumption prediction and applies it to two actual commercial buildings. Time series analysis is adopted as a main methodology to produce the data-driven model based on monthly actual energy consumption data. The models can be used to predict building future energy consumption, after being modified and verified.
The literature of network traffic analysis has successfully investigated several sophisticated models to be used in computer network traffic forecasting. Although these models have shown very good results in many controlled studies, the complexity of their implementation may be an important factor for preventing their large adoption in real production environment. We advocate that simpler forecasting...
This paper discusses the application of the time series AR (Auto Regressive) model in price forecast. It focuses on feasibility from the theoretical perspective and then proves the advantages of the application in price forecast by case studies. It shows that AR model is applicable in the forecasts of the trends in price statistics, the evaluation of physical conditions and the
Traffic prediction is of significant importance for telecommunication network planning and network optimization. In this paper, the traffic series from a certain mobile network in Heilongjiang province in China is studied. The characteristics in respect of both trend and periodicity are explored with autocorrelation function. Based on the characteristics exhibited in the traffic series, multiplicative...
In civil engineering, synthesized deformation prediction of deep foundation pit is a very complicated problem. For it belongs to a multi-variable nonlinear system evolution with time-varying, an enhanced back propagation (BP) neural network model based on multi-variable phase-space reconstruction has been proposed. By the various time series time delay and embedding dimension determined respectively...
In this paper, we describe a chaotic time series prediction using a combination of an echo state network (ESN) and a radial basis function network (RBFN). The ESN is a neural network consisting of three layers, where the hidden layer (the “reservoir”) is composed of many neurons. The RBFN is a neural network using a radial basis function (RBF) for its output function. We propose a neural network model...
Long-term time series prediction is to predict the future values multi-step ahead. It has received more and more attention due to its applications in predicting stock prices, traffic status, power consumption, etc. In this paper, a k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) framework is proposed to perform long-term time series prediction. A new distance function,...
The paper presents a method for prediction of multivariate chaotic time series, using radial basis function (RBF) neural network with the input phase space preprocessed by independent component analysis (ICA). Firstly, C-C method is used to respectively compute the embedding dimension and delay time for all variables, and we get a reconstructed initial multivariate input vector space which may be...
Semi-parametric regression model prediction method based on empirical mode decomposition was studied in this paper. Firstly, basic idea of the empirical mode decomposition was introduced, and the improved algorithm was proposed to mitigate the end effect in the iterative shift process. Secondly, least squares method was employed to estimate the parameter β based on the trend component of empirical...
Based on the analysis to the collected traffic from many WLAN testbed, a statistical model is proposed to predict the short-term traffic in IEEE 802.11 networks. By large numbers of differencing and sampling to the original data sequence, the season property was found and verified. Then, a time series model was given which can accurately predict the WLAN traffic, multiple seasonal arima model (0,...
The forecast on the time series of the parameter -varying chaotic system using LS-SVM was researched in this paper. The SVM method is built on the structural risk minimum theory. The least square support vector machine (LS-SVM) is one kind of SVM, which solvers the problem using the equal restriction because of adopting the quadratic loss function. The LS-SVM holds the virtue of classical SVM and...
One of the most important goals of time series analysis is prediction basing on the analyzed information. But it is not easy to analyze the patterns, regularities and trends of non-stationary and/or chaos time series because their major characteristics are non-linear and vague. In this paper, we propose primary and secondary tuning procedures that can enhance the accuracy for designing fuzzy prediction...
Time series analysis is used in various fields such as not only in economics but also in pattern recognition, biometrics, and Kansei engineering field. The problem of predicting time series can be classified into three in a practical sense. The first problem is how to make a model for prediction, that adequately represents the characteristics of the past time series data. The second problem is how...
The mathematical model of population growth is based on logistic equation and BP neural network. The total population is predicted for the next 30 years through the use of Logistic modeling and generalization data. The dynamics of population growth is studied in non-linear dynamic, which pointed out that the problem is the issue of chaos. It is difficult to accurately forecast long-term population...
The electrical life is an important index to evaluate the reliability of relay, and it is closely related with many characteristic parameters of relay such as contact resistance, pick-up time, over-travel time, etc. By using the time series analysis and by taking some characteristic parameters as predicted variables, the life of relay can be obtained by the life prediction which is a nondestructive...
It is very important in a lot of applications to forecast future trend of data streams. Recent works on prediction analysis over data streams mainly supposed that data are complete and data occur at equal time interval. Adopting state transition of time series and Kalman filter, a predictive model for forecasting the trend of data stream with missing values and data occurring in random time interval...
In this paper, we compare the costs and efficiency arising from the delta-neutral dynamic hedging of options, using two possible values for the delta of the option. The first one is the traditional Black-Scholes delta, while the second one is the GARCH option pricing delta, namely the delta of the option in the generalized Black-Scholes model with a volatility calibrated from GARCH model. Both our...
Trust is very important to semantic Web. We propose a trust model of semantic Web by using classic linear regression models, time series models and vector auto regression generalized autoregressive conditional heteroskedasticity models. First, we discuss the background and related work about trust in semantic Web. Then the management of uncertainty is analyzed. At last, we give the prediction algorithm...
A prediction model for short-time traffic flow series is proposed in this paper. At first, estimation of the largest Lyapunov exponent is implemented by applying small data sets method so as to validate that chaos exists in traffic flow series. Then, through properly choosing the delay time and the embedding dimension using mutual information and false nearest neighbor methods, respectively, phase...
This paper addresses the importance of electricity price forecasting in the deregulated electricity market. A simple multiple linear regression approach is proposed to predict next daypsilas electricity prices. The developed models are tested using time-series data of the Nordic electricity market (Nord Pool) and a Canadian electricity market (Ontario) and satisfactory results are achieved. The obtained...
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