Using machine learning for investment has always been a popular subject. In recent years, deep learning models have attracted a lot of attention, especially in the field of computer vision. Therefore, the paper introduced here provides a brand new approach by using the currently hottest computer vision neural network: Convolutional Neural Network (CNN) to predict … Read more
Sea Temperature Forecast Model Based on PCA and LSTM Full text can be downloaded on PC at the following address: http://www.hyyb.org.cn/Magazine/Show.aspx?ID=3464 Reading Notes Authors: Li Jingshi1 2 Kuang Xiaodi1 2 Li Qiong3 He Enye1 2 Zhang Yubai3 Yuan Chengyi4 Zhang Yanlin5 Affiliations: 1. National Marine Environmental Forecasting Center, Beijing 100081; 2. Key Laboratory of Marine … Read more
Research on LSTM Water Level Prediction Model Based on Improved Attention Mechanism Ma Fei 1, Tu Zhenyu 1*, Zhu Songting 2, Xiang Xinyue 1, Sun Yifei 1, Fang Qiang 1 (1. School of Information Engineering, Nanchang Engineering College, Nanchang, Jiangxi, 330099; 2. Jiangxi Flood Control Information Center, Nanchang, Jiangxi, 330009) Abstract In order to further … Read more
♚ Author: Yishui Hancheng, CSDN Blog Expert, Research Directions: Machine Learning, Deep Learning, NLP, CV Blog: http://yishuihancheng.blog.csdn.net Traditional linear models struggle with multivariate or multi-input problems, whereas neural networks like LSTM excel at handling multiple variables, making them suitable for time series prediction tasks. In the following article, you will learn how to build an … Read more
Research on LSTM Model for River Dissolved Oxygen Prediction Optimized by Attention Mechanism Zhou Quan1, Hu Xuanming2, Wang Dongkun2, Zhang Wucai1, Chen Zhongying1, Wang Jinpeng1, Wang Pengyang2, Ren Xiuwen1 1. South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Key Laboratory of Water Environment Simulation and Pollution Control, Guangzhou, Guangdong 5105302. University of … Read more
Source:DeepHub IMBA Complete code and detailed explanation for end-to-end time series forecasting using LSTM. First, let’s understand two topics: What is Time Series Analysis? What is LSTM? Time Series Analysis: A time series represents a series of data points indexed in time order. It can be in seconds, minutes, hours, days, weeks, months, or years. … Read more
Research on PDO Index Prediction Based on Multivariate LSTM Neural Network Model Scroll up and down to read more content Abstract Using sea level pressure, sea level height, heat content data from 1921–2020, and sea ice concentration as forecasting factors for the Pacific Decadal Oscillation (PDO) index, a multivariate Long Short Term Memory (LSTM) neural … Read more
Author: Sharmistha Chatterjee Translator: Chen Zhiyan Proofreader: Wu Jindi This article is approximately 5500 words, and it is recommended to read for 10+ minutes. This article discusses issues related to ensemble learning with simple ARIMA/SARIMA and LSTM time series. Sharmistha Chatterjee https://towardsdatascience.com/@sharmi.chatterjee Motivation The five most commonly used time series models in traditional time series … Read more
Today, I would like to introduce a powerful deep learning model: CNN-LSTM-Attention! This model combines three different types of neural network architectures, fully exploiting the spatial and temporal information in the data. It not only captures the local features and long-term dependencies of the data but also automatically focuses on the most important parts of … Read more
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning and predicting long sequences. In addition to learning long sequences, LSTMs can also learn to make multi-step predictions, which is very useful for time series forecasting. One challenge with LSTMs is that they can be difficult to configure and require … Read more