Time series data exist in various systems and affect the following management and control, in which real time series data sets are often composed of multiple variables. For predicting the future of...doi:10.1007/978-981-15-0474-7_59Xuebo JinXinghong YuXiaoyi WangYuting BaiJianlei Kon...
B. LSTM for Time series Prediction LSTM神经网络的输入是序列,它们是CNN模型的输出。每个序列分为多个元素。在每个时间步长,一个元素用作输入。如图3所示,空白圆圈代表状态,灰色圆圈代表输入。如果按照时间步长展开LSTM,则可以将LSTM表示为网络,如图3右侧所示。每个时间步长的输出和输入表示为oi和xi。
Although research on time series prediction based on deep learning is being actively carried out in various industries, deep learning technology still has a high entry barrier for researchers who have not majored in computer science. This paper presents a tutorial on time series prediction using a ...
Bay, The uci kdd archive, Irvine, CA: University of California, Department of Information and Computer Science. URL: http://kdd.ics.uci.edu. Google Scholar Cited by (6) Fusing anomaly detection with false positive mitigation methodology for predictive maintenance under multivariate time series ...
Stock price prediction; PyTorch; CNN; LSTM Abstract The stock market, as the main financing channel for listed companies and the most accessible wealth creation opportunity for investors, has always attracted attention from all walks of life. With the evolution of the technology, deep learning has...
Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems Article Open access 28 February 2024 Introduction In drilling engineering, ROP is a common metric used to assess the energy efficiency of drilling operations. In recent years, scholars have made si...
forQflowtime series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models.Qflowprediction is conducted for different time intervals with the length of 1-Week, 2-...
useful network on its own, but rather is meant to be integrated into a more extensive network, and to be trained to work in tandem with it in order to produce an end result. The CNN layer's responsibility is to extract meaningful sub-structures that are useful for the overall prediction ...
and "input gate" to time series data through simplified calculation. This paper presents an AQI prediction model based on CNN-ILSTM. In the model, CNN can achieve eigenvalues extraction well and make up for the insufficient feature extraction and learning of ILSTM. The experiment introduces ...
The CNN serves for the stock selection strategy, automatically extracts features based on quantitative data, then follows an LSTM to preserve the time-series features for improving profits. The latest work also proposes a similar hybrid neural network architecture, integrating a convolutional neural ...