Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Modeling polypharmacy effects with heterogeneous signed graph convolutional networks

Applied Intelligence - Pharmaceutical drug combinations can effectively treat various medical conditions. However, some combinations can cause serious adverse drug reactions (ADR). Therefore,...     

GRU-based capsule network with an improved loss for personnel performance prediction

Applied Intelligence - Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human...     

Spatial division networks for weakly supervised detection

Neural Computing and Applications - With only global image-level annotations, weakly supervised learning of deep convolutional neural networks has shown enough capacity in classification and...     

Closed‐loop control for self‐calibration of accelerometer achieved through integrated sensor and actuator system

Microsystem Technologies - In-situ self-calibration can fundamentally solve the problem of long-term stability of the accelerometer. The implementation of external integrated micro structure to...     

An improved generative adversarial network for translating clothes from the human body to tiled image

Neural Computing and Applications - Nowadays, purchasing the products similar to the styles of stars’ products has become a new trend in e-commerce platforms. Clothing transaction constitutes...     

An iterative gradient convolutional neural network and its application in endoscopic photoacoustic image formation from incomplete acoustic measurement

Neural Computing and Applications - Endoscopic photoacoustic tomography (EPAT) is a rapidly developing catheter-based imaging technique to provide cross-sectional images of anatomical, functional...     

RSA数字签名技术在信息化工程监理流程E化中的研究

提出的信息化工程监理流程的E化过程就是将监理流程通过现有的IT技术进行重新梳理、再造,达到电子化、网络化。将对在E化过程中数字签名的应用进行研究,以应对E化容易出现的伪造这一网络安全问题。主要讲述hash签名技术中单向散列函数对文件进行摘要处理,然后使用RSA公钥加密算法对摘要进行加密。也会对签名过程和验证过程给予解说。     

基于计算机视觉的油封尺寸检测系统设计

针对油封的大批量、多品种、流水线作业的生产特点以及"零废品率"的检测要求,利用机器视觉检测和计算机图象处理技术,研究开发了适应大批量生产和在线实时检测的油封尺寸机器视觉检测系统;建立了系统软硬件结构,分析了系统的组成和工作原理及工作过程,并对图像处理算法进行了设计;系统标定后的实验检测结果证明,该检测系统能实现产品100%检测的生产目标,具有非接触、在线实时、精度适中、成本低等特点,具有较好的生产通用性。     

基于xPC的气压ABS调节器动态参数测控系统开发

对反映气制动ABS调节器性能的静态参数和动态参数进行分析,提出基于xPC实时内核技术的动态数据采集系统设计;针对测试系统实现的关键技术进行了研究,主要包括测试软件界面设计及参数设置、通讯接口模块设计、数据实时保存与显示系统软件设计等;通过长期测试试验表明,系统采样频率可达1kHz,完全满足行业标准要求,从而证明系统开发的有效性和可靠性。