Volatility of international crude oil prices is influenced by various external factors on different time scales. User search data (USD) which reflects investor attentions has been widely researched and proved to be associated with crude oil price change at different frequency bands. In this paper, a novel hybrid approach that utilizes bivariate empirical mode decomposition (BEMD) with user search data and machine learning is developed for crude oil price forecasting. First, BEMD is adopted to simultaneously decomposed the crude oil price data and USD into a finite set of components. Second, each component is modelled and predicted by random vector functional link (RVFL) network and the corresponding final results are obtained via an ensemble model. Third, Brent crude oil spot price is used to test the proposed approach empirically. Forecasting results are analyzed with various evaluation criteria and verified robustness. Results show that the proposed approach statistically outperforms traditional forecasting machine learning techniques and similar counterparts (with USD or EMD-based method) in terms of prediction accuracy.

Stock index forecast is a complicated problem since the financial market is influenced by various underlying factors and some of them are unobservable. Hidden Markov Model (HMM) is an effective probabilistic graphic model which can models the hidden states of the observed sequence. So the underlying patterns of the stock movements can be learnt by HMM models. In this paper, HMM is employed to forecast the Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Composite Index, which are the two representative stock indices in China. The proposed method is empirically tested on the target data sets and compared with other models based on HMM using Mean Absolute Percentage Error (MAPE). Results show that the proposed HMM model achieves good forecasting performance, and at the same time reveals meaningful hidden states of stock market.

The queries entered into search engines register hundreds of millions of different searches by tourists, not only reflecting the trends of the searchers' preferences for travel products, but also offering a forecasting of their future travel behavior. This paper proposed a forecasting framework based on internet search index and machine learning to forecast tourist arrivals, and compared the forecasting performance of two different search engines data, Baidu and Google. The empirical results suggest that the proposed KELM models by fusing Baidu index and Google index can significantly improve the forecasting performance and outperform other benchmark models in terms of forecasting accuracy.

The S&P 500 index is an important representative of worlds' financial market and is influenced by various economic factors. There is a call for automatically select antecedents of S&P 500 index's change in the fast-changing world economy. This paper proposes an enhanced GSA model named LGSA to solve the feature selection and parameter optimization of SVM models for the S&P 500 index movement prediction. The results show that the accuracy of LGSA-SVM model surpasses benchmark SVM, PSO-SVM and GA-SVM model. And the proposed approach could hopefully be adopted for other financial data series automatic forecasting.

With the wide use of Internet, search data are expected to reflect people's expectations and be used as a predictor for selective macro-economic indicators. In order to improve the accuracy of forecasting, as high frequency data, Google search volume index (GSVI) has been used to forecast macro-economic variables. This paper applies the Panel Vector Autoregressive Model (PVAR) to examine the dynamic relationship between GSVI and Consumer Price Index (CPI). A Mixed Data Sampling (MIDAS) model with GSVI is proposed to forecast CPI of 13 countries, including 9 developed and 4 developing economics. Empirical results indicate that GSVI has high correlation with CPI; the response of GSVI to a CPI shock is positive and strongly significant, vice versa. However, the feedback of GSVI to a CPI shock is much stronger for developing economies compared with developed economies. And the paper proves the usefulness of search data for inflation forecasting with 13 countries' monthly inflation data from June 2012 to June 2017, the MIDAS models including GSVI outperform the multivariate regression models on average and verify the effectiveness of treating search data as an efficient indicator for inflation forecasting.

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back-propagation neural network (BPNN)-based forex forecasting subsystem and Web-based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e-service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.

Data preparation is an important and critical step in neural network modeling for complex data analysis and it has a huge impact on the success of a wide variety of complex data analysis tasks, such as data mining and knowledge discovery. Although data preparation in neural network data analysis is important, some existing literature about the neural network data preparation are scattered, and there is no systematic study about data preparation for neural network data analysis. In this study, we first propose an integrated data preparation scheme as a systematic study for neural network data analysis. In the integrated scheme, a survey of data preparation, focusing on problems with the data and corresponding processing techniques, is then provided. Meantime, some intelligent data preparation solution to some important issues and dilemmas with the integrated scheme are discussed in detail. Subsequently, a cost-benefit analysis framework for this integrated scheme is presented to analyze the effect of data preparation on complex data analysis. Finally, a typical example of complex data analysis from the financial domain is provided in order to show the application of data preparation techniques and to demonstrate the impact of data preparation on complex data analysis.