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...
Deep learning for stock prediction using numerical and textual information This paper proposes a novel application of deep learning models, Paragraph Vector, and Long Short-Term Memory (LSTM), to financial time series forecasting... R Akita,A Yoshihara,T Matsubara,... - IEEE/ACIS International Co...
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。
It is my understanding that you are trying to integrate a Squeeze-and-Excitation (SE) block into an LSTM network for time series prediction in MATLAB. You can create a custom function to implement the SE block logic for LSTM outputs, and modify the LSTM Network to includ...
The 3DCNN is combined with Long short team memory (LSTM) and Bidirectional LSTM for prediction of abnormal events from past observations of events in video stream. It is observed that 3DCNN with LSTM resulted in increased accuracy compared to 3DCNN with Bidirectional LSTM. The experiments were ...
To overcome those issues, we propose a deep learning framework named Tensor-CNN-LSTM (TCL) in this paper, which can extract travel speed effectively from historical sparse trajectory data and predict travel time with better accuracy. Empirical results over two real-world large-scale datasets show ...
In this paper, we design and apply the Long Short-Term Memory (LSTM) neural network approach to predict several financial classes’ time series under COVID-19 pandemic crisis period. We use the S&P GSCI commodity indices and their sub-indices and consider the stock market indices for different...
A deep LSTM-CNN based on self-attention mechanism with input data reduction for short-term load forecasting 2023, IET Generation, Transmission and Distribution Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events 2022, ...
It shows that some progress is got in addressing the problem of easy gradient disappearance of LSTM [19]. Applying deep learning to predict spatio-temporal sequence problems has become a hot topic, but the huge model size makes it difficult for practical applications. At the same time, deep ...
with more layers. A series of new structures and new methods that can be worked and evolved. New structures include a CNN, LSTM, ResNet, etc. There are different units in CNN and LSTM, and there are mainly convolutional units [24] and the unit of pooling on CNN. There is mainly ...