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| 1 | SCONV:一种基于情感分析的金融市场趋势预测方法显示文摘股票市场是国家经济发展的重要组成部分,也是与我们日常生活息息相关的一个市场,股民的情绪一定程度上可以作为影响股票价格的因素之一.提出一种基于ConvLstm(convolutional long short term memory)的股票情感分析价格预测的深度学习模型SCONV(semantic convolutional).该模型通过爬取股民评价,使用LSTM(long short term memory)模型并通过word2vec,进行情感分析,提取情感向量,并得出每一日的情感权重.随后将每日股价分别与对应前1日、前3日均值、前一周均值的情感权重与股票价格一起放入ConvLstm中进行训练,再使用叠加的一层LSTM来增加准确率,并在ConvLstm与增加的LSTM之间增加dropout层,来避免过拟合.实验数据采用了3年左右阿里巴巴(BABA.us)、1.5年左右平安银行(000001.sh)、5个月左右格力电器(000651.sz),实验结果表明:相比一些传统模型,SCONV在较小的样本集上依旧可以更好地预测股票价格的走势. | 林培光 周佳倩 温玉莲 | 2020 | 计算机研究与发展2020，57，8： | 8 |
| 2 | 基于时间序列模型的医院门诊量分析与预测显示文摘医院门诊量分析与预测对医疗资源管理和为高质量医疗护理提供决策有重要作用.当前在门诊量分析与预测方面的研究还没引起足够重视,且研究主要集中在门诊量预测的计算方法,缺少全面深入的数据分析和规律挖掘.为此提出构建ARMAX模型、神经网络模型和ARMAX模型与神经网络的混合模型,用来描述医院门诊量的线性和非线性特征.以时间序列模型全面深入地分析厦门市医院门诊量日度数据的规律,研究发现,医院门诊量有显著的上升趋势、周内日效应以及很强的序列自相关性.通过样本外预测比较发现,采用混合模型进行预测取得的预测结果较好,这是由于混合模型能够同时获取门诊量数据的线性部分和非线性部分,数据信息比较完整. | 朱顺痣 王大寒 何亚男 王琰 | 2015 | 中国科学技术大学学报2015，45，10： | 8 |
| 3 | A Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average: An Application in Air Travel Demand Forecasting显示文摘Demand forecasting is often difficult due to the unobservability of the applicable historical demand series. In this study, the authors propose a demand forecasting method based on stochastic frontier analysis(SFA) models and a model average technique. First, considering model uncertainty,a set of alternative SFA models with various combinations of explanatory variables and distribution assumptions are constructed to estimate demands. Second, an average estimate from the estimated demand values is obtained using a model average technique. Finally, future demand forecasts are achieved, with the average estimates used as historical observations. An empirical application of air travel demand forecasting is implemented. The results of a forecasting performance comparison show that in addition to its ability to estimate demand, the proposed method outperforms other common methods in terms of forecasting passenger traffic. | ZHANG Xinyu ZHENG Yafei WANG Shouyang | 2019 | Journal of Systems Science & Complexity2019，32，2： | 5 |
| 4 | Profit Guided or Statistical Error Guided? A Study of Stock Index Forecasting Using Support Vector Regression显示文摘Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue that statistical metrics used to guide parameter tuning of forecasting models may not be meaningful, given the fact that the ultimate goal of forecasting is to facilitate investment decisions with expected profits in the future. The authors therefore introduce the Sharpe ratio into the process of model building and take it as the profit metric to guide parameter tuning rather than using the commonly adopted statistical metrics. The authors consider three widely used trading strategies, which include a na¨?ve strategy, a filter strategy and a dual moving average strategy, as investment scenarios. To verify the effectiveness of the proposed profit guided approach, the authors carry out simulation experiments using three global mainstream stock market indices. The results show that profit guided forecasting models are competitive, and in many cases produce significantly better performances than statistical error guided models. This implies thatprofit guided stock index forecasting is a worthwhile alternative over traditional stock index forecasting practices. | HU Zhongyi BAO Yukun CHIONG Raymond XIONG Tao | 2017 | Journal of Systems Science & Complexity2017，30，6： | 1 |
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