This article is an excellent piece from the KX Forum, author ID: Flying Fish Oil 1 Introduction In recent years, the rapid development of natural language processing has introduced a series of related algorithms and models. For example, RNN (Recurrent Neural Network) for processing sequential data, LSTM (Long Short-Term Memory Network), GRU (Gated Recurrent Unit), … Read more
Authors Wei Yan, Yang Chunying, Wang Yongfang, Liu Pengfei Technology Readiness Level (TRL) is a widely used method for quantifying the technological development progress of major scientific and engineering projects, indicating the development status of a technology relative to the system or the entire project. Technology maturity assessment is a systematic standard, method, and tool … 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
Source: DeepHub IMBA This article is about 3400 words long and is recommended to read for more than 10 minutes. This article will guide you to recognize human activities using raw data generated by mobile sensors. Human Activity Recognition (HAR) is a method that uses Artificial Intelligence (AI) to recognize human activities from raw data … Read more
Source：Deephub Imba This article is approximately 4000 words, and it is recommended to read in 10minutes In this tutorial, we will use PyTorch-LSTM for deep learning time series prediction. A time series refers to any quantifiable measurement or event that occurs over a period of time. Although this may sound trivial, almost anything can be … Read more
Author Information Liuyishi1, Guan Xuefeng1,2, Wu Huayi1,2, Cao Jun1, Zhang Na1 (1. Key Laboratory of Surveying and Mapping Remote Sensing Information Engineering, Wuhan University, Wuhan, Hubei 430079; 2. Collaborative Innovation Center of Geospatial Information Technology, Wuhan, Hubei 430079) “ AbstractA short-term traffic speed prediction model based on spatio-temporal correlation weighting using Long Short-Term Memory (LSTM) … Read more
Click the blue words to follow us Research on theLSTM River Dissolved Oxygen Prediction Model Optimized by Attention Mechanism Zhou Quan1,2, Hu Xuanming3, Wang Dongkun3, Zhang Wucai1,2, Chen Zhongying1,2, Wang Jinpeng1,2, Wang Pengyang3*, Ren Xiuwen1,2* 1. South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, Guangdong 510530 2. Key Laboratory of Water … 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
First, I recommend a Jupyter environment, which is provided by Google called colab (https://colab.research.google.com/), where you can use free GPUs. The first time you use it, you need to download the relevant Python libraries in the experimental environment. !pip install torch!pip install torchtext!python -m spacy download en Our preliminary idea is to first input a … 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