共查询到20条相似文献，搜索用时 46 毫秒
1.
农产品市场价格预测是研究的难点.本文采用蔬菜市场价格数据,分别建立了BP神经网络模型、基于遗传算法的神经网络模型、RBF神经网络模型,并在前三种模型基础上,建立了一种集成预测模型;用北京市批发市场2003-2007年的蔬菜价格训练模型,对2008-2009年的数据进行了预报,前三种模型预报结果的平均绝对误差分别为0.1... 相似文献
2.
物料库存预测是企业经营管理的重要方面,它直接影响企业的生产与销售以及企业经济效益的实现。开展物料安全库存预测研究对于合理地控制物料的进出,节约存货空间,降低库存成本,提高库存管理的科学性和企业经济效益具有重要的理论意义与实际应用价值。文章构建了基于粒子群优化的神经网络的手机物料库存预测模型,实验结果表明,手机物料库存的预测值与实际值吻合度较好,该方法可有效地提高预测准确度,对实际生产具有一定的指导作用。 相似文献
3.
4.
《Information processing & management》2020,57(5):102212
Stock prediction via market data analysis is an attractive research topic. Both stock prices and news articles have been employed in the prediction processes. However, how to combine technical indicators from stock prices and news sentiments from textual news articles, and make the prediction model be able to learn sequential information within time series in an intelligent way, is still an unsolved problem. In this paper, we build up a stock prediction system and propose an approach that 1) represents numerical price data by technical indicators via technical analysis, and represents textual news articles by sentiment vectors via sentiment analysis, 2) setup a layered deep learning model to learn the sequential information within market snapshot series which is constructed by the technical indicators and news sentiments, 3) setup a fully connected neural network to make stock predictions. Experiments have been conducted on more than five years of Hong Kong Stock Exchange data using four different sentiment dictionaries, and results show that 1) the proposed approach outperforms the baselines in both validation and test sets using two different evaluation metrics, 2) models incorporating prices and news sentiments outperform models that only use either technical indicators or news sentiments, in both individual stock level and sector level, 3) among the four sentiment dictionaries, finance domain-specific sentiment dictionary (Loughran–McDonald Financial Dictionary) models the news sentiments better, which brings more prediction performance improvements than the other three dictionaries. 相似文献
5.
Enric Junqué de Fortuny Tom De Smedt David Martens Walter Daelemans 《Information processing & management》2014
Despite the fact that both the Efficient Market Hypothesis and Random Walk Theory postulate that it is impossible to predict future stock prices based on currently available information, recent advances in empirical research have been proving the opposite by achieving what seems to be better than random prediction performance. We discuss some of the (dis)advantages of the most widely used performance metrics and conclude that is difficult to assess the external validity of performance using some of these measures. Moreover, there remain many questions as to the real-world applicability of these empirical models. In the first part of this study we design novel stock price prediction models, based on state-of-the-art text-mining techniques to assert whether we can predict the movement of stock prices more accurately by including indicators of irrationality. Along with this, we discuss which metrics are most appropriate for which scenarios in order to evaluate the models. Finally, we discuss how to gain insight into text-mining-based stock price prediction models in order to evaluate, validate and refine the models. 相似文献
6.
以1996—2020年间智能物流产业专利数据为研究对象，分析智能物流技术跨领域融合的独立发展模式、单向型融合模式、顾问式双向融合模式、联络人式双向融合模式，并在此基础上基于局部相似性，从Jacard指标、PA指标、RA指标进行融合趋势预测。研究结果表明，不同类型的融合模式呈现稳定、微小波动趋势；联络人式双向融合是主要方式；基于不同指标计算的预测结果显示，专用机械、金属制品、信号传输等技术是未来主要融合方向，专用机械-金属制品、专用机械-信号传输等之间的融合是未来主要趋势。 相似文献
7.
《Information processing & management》2022,59(3):102908
With the implementation of the innovation-driven development strategy, increasing technical innovations are patented by the individuals or the companies. As a form of intellectual properties, the patent has attracted attention from individuals and companies. Although there are some researches on the economic function of patent, few quantitative researches discuss on whether patents can work on the company stock market. To discover the relations between the company patents and the stock market, we explore a method to analyze the influence of patent activity on the company stock market. We collect the patent data and the stock data of listed companies, from which patent and market activities are extracted. By the recursive discrete wavelet transform, the patent and market activities are decomposed into multi-scale wavelets. These wavelets are fed into a patent and market activity based stock market trend prediction model, in which the influences of patent activity are analyzed. We compare our model with the state-of-the-art model on 4 measurements for 3 manufacturing datasets. The experimental results show that the patent activities have positive effect on market trend prediction in about 30% manufacturing listed companies and that the measurements of Shanghai/Shenzhen Stock Exchange often outperform that of USA in years 2016–2019 for the manufacturing listed companies. 相似文献
8.
DNA序列分析法在金融数据时间序列中的应用 总被引：5，自引：0，他引：5
通过线性分段将连续性的金融时间序列转化为离散性的字符序列,并基于DNA序列分析法,讨论了此类字符序列的标度特性,以及在金融数据时间序列预测中的可能应用 相似文献
9.
《Information processing & management》2023,60(3):103328
Stock movement forecasting is usually formalized as a sequence prediction task based on time series data. Recently, more and more deep learning models are used to fit the dynamic stock time series with good nonlinear mapping ability, but not much of them attempt to unveil a market system’s internal dynamics. For instance, the driving force (state) behind the stock rise may be the company’s good profitability or concept marketing, and it is helpful to judge the future trend of the stock. To address this issue, we regard the explored pattern as an organic component of the hidden mechanism. Considering the effective hidden state discovery ability of the Hidden Markov Model (HMM), we aim to integrate it into the training process of the deep learning model. Specifically, we propose a deep learning framework called Hidden Markov Model-Attentive LSTM (HMM-ALSTM) to model stock time series data, which guides the hidden state learning of deep learning methods via the market’s pattern (learned by HMM) that generates time series data. What is more, a large number of experiments on 6 real-world data sets and 13 stock prediction baselines for predicting stock movement and return rate are implemented. Our proposed HMM-ALSTM achieves an average 10% improvement on all data sets compared to the best baseline. 相似文献
10.
首先通过主成分分析消除原始指标之间的相关性，使指标数量变少且相互之间不相关，从而构建综合预判指标，再利用BP神经网络建立微博舆情预判模型。实验选取2013年微博热门话题作为训练样本，选取2014年的话题作为预测。实验结果表明，主成分分析有助于去除原始样本数据的冗余，简化了网络的复杂度，所得到的结果更加准确。因此，该模型较仅使用BP神经网络的准确性更高。 相似文献
11.
12.
13.
人工神经网络在地球物理领域中，尤其在模式识别和油气预测方面得到了较好的应用．前向网络的重要特性是能够总结、归纳已知样本隐含的函数关系．然而其推广性能有待进一步研究．本文强调了该问题的重要性并提出了改善网络推广性能的技术，即在网络学习过程中，不仅让总误差下降，还尽可能使建立的“隐函数”平滑．计算实例表明，本文的算法可以明显地改善网络的推广性能．最后给出了用该技术在辽河油田进行油气预测的实例 相似文献
14.
为克服传统方法的局限性，本文尝试以企业竞争情报内容和情报搜集活动过程作为情报搜集成本分析的基础，在此基础上引入BP神经网络进行预测，并用部分样本数据验证对比了线性回归分析法和BP神经网络的预测结果。验证结果表明，BP神经网络预测模型用于情报搜集成本的预测具有较高的预测精度。 相似文献
15.
基于支持向量机的股票投资价值分类模型研究 总被引：1，自引：0，他引：1
本文遵循价值投资理念,建立基于支持向量机的股票投资价值分类模型。首先随机抽取500支A股股票作为样本,并选取对股票投资价值影响显著的财务指标构造样本特征集,然后采用支持向量机方法建立股票投资价值分类模型,最后将其与BP神经网络和RBF神经网络相比较,结果表明支持向量机的分类效果和泛化能力最优。 相似文献
16.
《Information processing & management》2023,60(4):103358
Stock forecasting has always been challenging as the stock market is affected by a combination of factors. Temporal Convolutional Network (TCN) based on convolutional structure has been widely used in time series prediction in recent years, but the dilated causal convolution structure leaves it unable to effectively learn the dependencies between data at different time points. This paper proposes a method for stock ranking prediction. To enhance the ability of TCN to handle dependencies within series, we first develop a channel-time dual attention module (CTAM). In conjunction with TCN to process complex historical stock price data, CTAM can adaptively learn the importance of multiple price nature series of stocks and model the dependencies between the data at different times. On the other hand, due to the market industry rotation, some stocks with specific industry attributes may become market preference for a period time. To apply the industry attributes to the stock prediction, we construct an industry-stock Pearson correlation matrix and extract a vector that fully characterizes the industry attributes of stocks from it through a matrix factorization algorithm. Furthermore, the historical market preference is modeled according to the industry attribute of the stocks to generate the dynamic correlation between stocks and market preference, and this correlation is combined with the historical price features extracted by TCN for stock ranking prediction. We conduct experiments on three datasets of 950 constituent stocks of the Shanghai Stock Exchange Index, 750 constituent stocks of the Shenzhen Stock Exchange 1000 Index and 486 stocks of the S&P500 to demonstrate the effectiveness of the proposed method. On the Shanghai Stock Exchange Index dataset, the Investment Return Ratio (IRR) obtained by using the predict results of our method to guide the exchange reached 1.416, and the Sharpe Ratio (SR) reached 2.346. On the Shenzhen Stock Exchange Index dataset, the IRR reached 1.434 and the Sharpe ratio reached 2.317. On the S&P500, the IRR reached 1.491 and the Sharpe ratio reached 2.031. 相似文献
17.
随着信息技术的不断发展,应用商业智能技术进行数据挖掘与分析对商家来说也越来越重要,分类回归树和神经网络算法是数据挖掘的经典算法,其广泛运用在数据分析、预测和评估等方面。文章分别运用分类回归树和神经网络算法对零售商品采取促销方案后收入变化的数据进行分析,并建立相应的模型对促销方案效果进行预测。 相似文献
18.
本文从资源、供需、运输、灾害、环境、市场六个方面构建涵盖供给安全和使用安全在内的煤炭安全评价体系,应用BP神经网络进行中国煤炭安全评价的实证分析。研究发现:凭借BP神经网络在能源等非线性复杂系统高效的仿真能力和逆向输出的优势,仿真训练结果准确率高,模拟预测简便易行。虽然丰富的资源储量和较高的自给水平确保了煤炭的供给安全,但CO2和SO2排放造成严重的环境问题和温室效应,使得使用安全仍是煤炭安全需要关注的焦点。今后,需要在降低煤炭在能源结构中的比重,提升电煤消费份额,大力发展洁净煤技术等方面采取有效的应对措施。 相似文献
19.
基于详实的历史数据和合理的预测模型,科学预测中国锡金属消费趋势,对于国家锡资源管理政策的制定与提升国家资源保障能力具有毋庸置疑的意义。在充分考虑影响锡金属消费的宏观经济环境、中观产业政策以及微观消费市场的基础上,采用灰色关联度分析模型,选取了GDP、空调产量、罐头产量、汽车产量和彩色电视机产量等5个关联度>75%的线性因子支撑BP神经网络预测。BP神经网络模型测算得出2002-2013年我国锡消费量的相对误差最大为10.78%,相对误差绝对值平均数为3.33%,对中长期而言精度较高。预测结果显示参考情景下到2020年、2025年及2030年中国锡金属消费需求量分别为26.59万t、29.63万t及31.65万t。 相似文献
20.
We propose a coding scheme for the stabilization of continuous linear scalar systems under a communication channel with finite data rate, lossy observations and network-induced delay. Owing to the network-induced delay, uncertain control input is introduced to the corresponding discrete time systems. Furthermore, since the system state is quantized to finite-bit signals and may be randomly lost in the communication, under the proposed coding scheme, the considered scalar system is described by a switched system of more than one dimension with arbitrary switchings. We derive the conditions for first moment stability of the systems, characterizing the relations on quantization cell boundaries, packet loss probabilities, and the network-induced delay. Further, we derive the condition for almost sure stability of the systems. A numerical method is proposed to search the infimum of the data rate for first moment stability under the given condition. It is shown that for the quantizer with the infimum of the data rate for first moment stability, although the quantization cells have different lengths, the matrices corresponding to these quantization cells have the same maximum eigenvalue. 相似文献