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Polynomials have shown to be useful basis functions in the identification of nonlinear systems. However estimation of the unknown coefficients requires expensive algorithms, as for instance it occurs by applying an optimal least square approach. Bernstein polynomials have the property that the coefficients are the values of the function to be approximated at points in a fixed grid, thus avoiding a...
Air quality forecasting is of great significance in environmental science, because air pollution has adverse influence on human beings and the environment. In this paper, a model W-SVM combining wavelet technique and support vector machine (SVM) is developed to forecast daily PM10 concentration. Firstly, time series of PM10 concentration is decomposed into several different frequencies by the static...
The concept of internet finance has attracted increasing attention in recent years. As a result, more and more online peer-to-peer (P2P) lending platforms have been established at home and abroad. It is actually meaningful to predict investment amounts of online lenders in the following period. In this paper, we propose a Hybrid Investment Prediction Model (HIPM), an effective non-linear prediction...
This research work emphases on the prediction of future stock market index values based on historical data. The experimental evaluation is based on historical data of 10 years of two indices, namely, CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets. The predictions are made for 1–10, 15, 30, and 40 days in advance. This work proposes to combine the predictions/estimates...
Time series forecasting has a fundamental importance in various practical domains. Many models have been proposed in literature to model and predict the Time Series Data (TSD) efficiently. As the modeling and prediction depends on the nature of TSD, one model may not be opt for all applications. This paper presents a hybrid model based on Particle Swarm Optimization (PSO) with Least Square Support...
India is the third largest natural rubber producing country of the world, next to Thailand and Indonesia, producing about 9 per cent of the global output. Kerala is the largest natural rubber producing state in India. Among the major plantation crops, natural rubber occupies a major role in agriculture income of Kerala state. The frequent up and down in the rubber price affect the daily life of those...
The prediction of passenger volume of single inter-city train is one of main basis for railway administrations to make scientific decisions, to make a feasible plan, to reduce the risk to the greatest extent, to reduce the operating cost and to make full use of the facilities. Because of the complicated and nonlinear relationship between factors that affect passenger volume, it is difficult to ensure...
Classifying sequential data is an important problem in machine learning with applications in time series, sensor streams, and image analysis. The ordered structure of sequential data presents a difficulty for the standard classification models, which has motivated the task of generating features for vector-based discriminative models. Shapelet methods, which have been extensively studied in this topic,...
In this paper, we focus on the problem of how to design a methodology which can improve the prediction accuracy as well as speed up prediction process for stock market prediction. As market news and stock prices are commonly believed as two important market data sources, we present the design of our stock price prediction model based on those two data sources concurrently. Firstly, in order to get...
Supply Chain Management (SCM) plays a very vital role in managing and organizing enterprise processes, increasing operational efficiency of the organization. Factors such as product success, customer satisfaction, organization's growth depends upon successful execution of Supply Chain Management (SCM). Supply chain management is becoming a necessity to improve the foundation and infrastructure within...
Cyber bullying is a new phenomenon resulting from the advance of new communication technologies including the Internet, cell phones and Personal Digital Assistants. It is a challenging bullying problem occurring in a new territory. Online bullying can be particularly damaging and upsetting because it's usually anonymous or hard to trace. In this paper, the proposed method is utilizing a dataset of...
In this thesis, the main content of statistical learning theory is firstly introduced briefly, based on this, the basic principle and process of ∊-SVR (one algorithm of Support Vector Machine for Regression, SVR) is presented. Then this method is used to model tourist traffic prediction and predict one series data (Taian monthly tourist quantity data). Two different kernel functions...
In this paper, in order to overcome the deficiency of the traditional SVM, a positive mapping between price volatilities and sample periods of underlying financial time series has been assumed according to the theorems of behavioral finance. By embedding this mapping into the constraint equations of the classic SVM algorithm, an improved SVM model named DHC-SVM (Dynamic Heteroskedasticity Convertible...
This paper proposes an investment in the stock exchange of Thailand (SET) using ARIMA model and support vector machine. Today, the investors are interesting to invest the stock market because it provides for higher profits than ones from deposit banking. Although the stock market can provide a high benefit for investors, it comes with a high risk too. Thus, this is a reason why we are proposing ARIMA...
In this paper, a long-team immersion of concrete in dilute sulfuric acid is carried out. On the basis of the experimental data, a time serious prediction model of concrete corrosion in sulfuric based on support vector machine (SVM) is developed. The design steps and learning algorithm are also given. Comparing with the test result, this model has good predictive function, with the suitable reconstructed...
In the area of prognostics and health management, data-driven methods increasingly show the superiority against model-based method due to the complex relationships and learn trends available in the data captured without the need for specific failure models. This paper uses Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) to build a model for non-stationary time series prediction...
In time series classification, the nearest neighbor (NN) method is known to compare well against others over a wide range of benchmark data. However, adapting its instance-based learning format for application-specific goals, such as optimizing alternate performance measures or cost-sensitive learning is not a straightforward problem. In this paper, we attempt to extend the effectiveness of the NN...
The prediction of chaotic time series is performed by relevance vector machine (RVM), which is an inherent online machine learning technique utilizing a flexible and sparse function without additional regularization parameters. The main objective of this approach is to increase the accuracy of the chaotic time series prediction. The method is applied to Mackey-Glass and Lorenz equations, Henon mapping...
It should improve the forecasting accuracy in the study of precipitation prediction. It is difficultly to predict climate because of the dynamic characteristics of sample set as well as the effect of environmental factors. In order to improve the accuracy, a novel model based on time series and environmental factors was introduced in this paper. Firstly, the environmental factors were nonlinear screened...
Call Forecasting is the premise of staffing and scheduling in call center. This paper is based on the analysis of actual data and the comparison of various time series forecasting methods , proposed the hybrid algorithm which combining the SARIMA model and support vector machine model. We used the SARIMA(seasonal autoregressive integrated moving average) model with 48 periods and a input for the linear...
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