@article{yang_he_xu_ni_jones_samatova_2018, title={An Intelligent and Hybrid Weighted Fuzzy Time Series Model Based on Empirical Mode Decomposition for Financial Markets Forecasting}, volume={10933}, ISBN={["978-3-319-95785-2"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85049883593&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-95786-9_8}, abstractNote={Given the potentially high impact of accurate financial market forecasting, there has been considerable research on time series analysis for financial markets. We present a new Intelligent Hybrid Weighted Fuzzy (IHWF) time series model to improve forecasting accuracy in financial markets, which are complex nonlinear time-sensitive systems, influenced by many factors. The IHWF model uniquely combines Empirical Mode Decomposition (EMD) with a novel weighted fuzzy time series method. The model is enhanced by an Adaptive Sine-Cosine Human Learning Optimization (ASCHLO) algorithm to help find optimal parameters that further improve forecasting performance. EMD is a time series processing technique to extract the possible modes of various kinds of institutional and individual investors and traders, embedded in a given time series. Subsequently, the proposed weighted fuzzy time series method with chronological order based frequency and Neighborhood Volatility Direction (NVD) is analyzed and integrated with ASCHLO to determine the effective universe discourse, intervals and weights. In order to evaluate the performance of proposed model, we evaluate actual trading data of Taiwan Capitalization Weighted Stock Index (TAIEX) from 1990 to 2004 and the findings are compared with other well-known forecasting models. The results show that the proposed method outperforms the listing models in terms of accuracy.}, journal={ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS (ICDM 2018)}, author={Yang, Ruixin and He, Junyi and Xu, Mingyang and Ni, Haoqi and Jones, Paul and Samatova, Nagiza}, year={2018}, pages={104–118} }
 @article{xu_yang_jones_samatova_2018, title={Mining Aspect-Specific Opinions from Online Reviews Using a Latent Embedding Structured Topic Model}, volume={10762}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85055688176&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-77116-8_15}, abstractNote={Online reviews often contain user’s specific opinions on aspects (features) of items. These opinions are very useful to merchants and customers, but manually extracting them is time-consuming. Several topic models have been proposed to simultaneously extract item aspects and user’s opinions on the aspects, as well as to detect sentiment associated with the opinions. However, existing models tend to find poor aspect-opinion associations when limited examples of the required word co-occurrences are available in corpus. These models often also assign incorrect sentiment to words. In this paper, we propose a Latent embedding structured Opinion mining Topic model, called the LOT, which can simultaneously discover relevant aspect-level specific opinions from small or large numbers of reviews and to assign accurate sentiment to words. Experimental results for topic coherence, document sentiment classification, and a human evaluation all show that our proposed model achieves significant improvements over several state-of-the-art baselines.}, journal={COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2017, PT II}, author={Xu, Mingyang and Yang, Ruixin and Jones, Paul and Samatova, Nagiza F.}, year={2018}, pages={195–210} }
 @article{xu_yang_harenberg_samatova_2017, title={A Lifelong Learning Topic Model Structured Using Latent Embeddings}, ISSN={["2325-6516"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85018319146&partnerID=MN8TOARS}, DOI={10.1109/icsc.2017.15}, abstractNote={We propose a latent-embedding-structured lifelong learning topic model, called the LLT model, to discover coherent topics from a corpus. Specifically, we exploit latent word embeddings to structure our model and mine word correlation knowledge to assist in topic modeling. During each learning iteration, our model learns new word embeddings based on the topics generated in the previous learning iteration. Experimental results demonstrate that our LLT model is able to generate more coherent topics than state-of-the-art methods.}, journal={2017 11TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC)}, author={Xu, Mingyang and Yang, Ruixin and Harenberg, Steve and Samatova, Nagiza F.}, year={2017}, pages={260–261} }
 @article{yang_xu_he_ranshous_samatova_2017, title={An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting}, volume={10604}, ISBN={["978-3-319-69178-7"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85033718976&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-69179-4_42}, abstractNote={Financial forecasting is an extremely challenging task given the complex, nonlinear nature of financial market systems. To overcome this challenge, we present an intelligent weighted fuzzy time series model for financial forecasting, which uses a sine-cosine adaptive human learning optimization (SCHLO) algorithm to search for the optimal parameters for forecasting. New weighted operators that consider frequency based chronological order and stock volume are analyzed, and SCHLO is integrated to determine the effective intervals and weighting factors. Furthermore, a novel short-term trend repair operation is developed to complement the final forecasting process. Finally, the proposed model is applied to four world major trading markets: the Dow Jones Index (DJI), the German Stock Index (DAX), the Japanese Stock Index (NIKKEI), and Taiwan Stock Index (TAIEX). Experimental results show that our model is consistently more accurate than the state-of-the-art baseline methods. The easy implementation and effective forecasting performance suggest our proposed model could be a favorable market application prospect.}, journal={ADVANCED DATA MINING AND APPLICATIONS, ADMA 2017}, author={Yang, Ruixin and Xu, Mingyang and He, Junyi and Ranshous, Stephen and Samatova, Nagiza F.}, year={2017}, pages={595–607} }