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Cgboost
Cgboost





cgboost

In: 2013 IEEE 13th International Conference on Data Mining Workshops, pp. Porshnev, A., Redkin, I., Shevchenko, A.: Machine learning in prediction of stock market indicators based on historical data and data from Twitter sentiment analysis. Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining of news-headlines for forex market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. In: 2009 International Joint Conference on Neural Networks, pp. Martinez, L.C., da Hora, D.N., Palotti, J.R.d.M., Meira, W., Pappa, G.L.: From an artificial neural network to a stock market day-trading system: a case study on the BM&F BOVESPA. Lawrence, R.: Using neural networks to forecast stock market prices. Kim, K.J.: Toward global optimization of case-based reasoning systems for financial forecasting. Hsieh, T.J., Hsiao, H.F., Yeh, W.C.: Forecasting stock markets using wavelet transforms and recurrent neural networks: an integrated system based on artificial bee colony algorithm. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks.

cgboost

In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. Guo, Z., Wang, H., Liu, Q., Yang, J.: A feature fusion based forecasting model for financial time series.

cgboost

44(8), 1259–1268 (2013)Įmenike, K.O.: Forecasting Nigerian stock exchange returns: evidence from autoregressive integrated moving average (ARIMA) model. ACM (2016)ĭiao, R., Chao, F., Peng, T., Snooke, N., Shen, Q.: Feature selection inspired classifier ensemble reduction. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 153–160 (2007)Ĭhen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Advances in Neural Information Processing Systems, pp. PloS one 12(7), e0180,944 (2017)īengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. 18(2), 18 (2005)īao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. Altay, E., Satman, M.H.: Stock market forecasting: artificial neural network and linear regression comparison in an emerging market.







Cgboost