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Information fusion and S&P500 trend prediction
2013 Edition, May 1, 2013 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

The purpose of this study is the prediction of Standard & Poor's (S&P500) trends (ups and downs) with macroeconomic variables, technical indicators, and investor moods using k-NN algorithm and probabilistic neural networks. More precisely, eleven...

Topic Trend Prediction Based on Wavelet Transformation
2014 Edition, September 1, 2014 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

The research of topic trend prediction can be a good reference for maximizing the propagation effects of network advertisements as well as guiding and controlling the network consensus. This paper proposes PTEP (the Prediction of Topic Energy Peak) method to model the...

Power variation trend prediction in modern datacenter
2017 Edition, May 1, 2017 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

The complexity of management of modern datacenter is rapidly growing. It is emergent that the datacenter management system is able to automatically analyze datacenter status and provide intelligence for workload orchestration and infrastructure management. The state of the art...

Stock trends prediction by hypergraph modeling
2012 Edition, June 1, 2012 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

This paper presents a new stock price trends prediction algorithm using hypergraph model. Hypergraph modeling offers a significant advantage over traditional graph modeling in terms of triadic or higher relationship description within different stock portfolios over a certain period...

Trend prediction of internet public opinion based on collaborative filtering
2016 Edition, August 1, 2016 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Collaborative filtering recommendation has very important applications in the personalized recommendation. Especially it is widely used in e-commerce. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations and...

State trend prediction of spacecraft based on BP neural network
2013 Edition, Volume 02, August 1, 2013 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

According to the requirement of state trend prediction for spacecraft fault prediction, a spacecraft state trend prediction method is proposed based on BP neural network. The principle and model of BP neural network are introduced into spacecraft fault...

Temporal Topic Inference for Trend Prediction
2015 Edition, November 1, 2015 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Publicly available social data has been adoptedwidely to explore language of crowds and leverage themin real world problem predictions. In microblogs, usersextensively share information about their moods, topics ofinterests, and social events which provide ideal data...

Stock market trend prediction using a sparse Bayesian framework
2014 Edition, November 1, 2014 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

The aim of this study is to develop a relevance vector machine-a RVM classifier for trend prediction of the BELEX15 index of the Belgrade Stock Exchange. In addition, the RVM model is compared to two `similar' methods: support vector machines - SVMs and least squares support...

Based on the ZigBee greenhouse grey trend prediction control
2016 Edition, Volume 2, July 1, 2016 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Aiming at the problem of greenhouse control system that is difficult to achieve precise control, which is caused by the characteristics of model uncertainty, long time-delay, large inertia and nonlinear, etc. this paper puts forward grey trend prediction theory based on the...

An SVM-based approach for stock market trend prediction
2013 Edition, August 1, 2013 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

In this paper, an SVM-based approach is proposed for stock market trend prediction. The proposed approach consists of two parts: feature selection and prediction model. In the feature selection part, a correlation-based SVM filter is applied to rank and select a...

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