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
Author on Zhihu | master苏 Link | https://zhuanlan.zhihu.com/p/139617364 This article is approximately 3200 words, recommended reading 5 minutes This article introduces the visualization of the structure of LSTM models. I have recently been learning about the application of LSTM in time series prediction, but I encountered a significant issue: the structure of LSTM becomes very … Read more
Author: Siddharth M Translator: Wang Kehan Proofreader: Ouyang Jin This article is approximately 1300 words long and is recommended for a 6-minute read. This article teaches you how to use LSTM networks to predict stock price trends and visualize opening and closing prices. 1. Introduction Long Short-Term Memory (LSTM) is a model that can enhance … Read more
This article will introduce the use of a simple RNN model for time series prediction. For example, we currently have a segment of a sine curve as shown in the figure below. We will input the red part and train the model to output the next segment’s values. First, let’s analyze it. Assuming we input … Read more
Case Introduction This case will demonstrate how to use Recurrent Neural Networks (RNNs) for time series prediction. Specifically, we will use RNNs to predict the future values of a variable that depends on its own historical values. In this case, we will use a temperature dataset. We will provide the temperature values from the past … Read more
Click on the top "Xiaobai Learns Vision", select to add "Star" or "Pin" Heavyweight content delivered at the first time Introduction RNN (Recurrent Neural Network) is an artificial neural network with nodes oriented in a circular connection. Unlike feedforward neural networks, RNNs can utilize internal memory to process any sequential input series, meaning they learn … Read more
Source: Algorithm Advancement This article is approximately 11,000 words long and is recommended for a reading time of over 20 minutes. For complex nonlinear patterns, deep learning models have strong expressive capabilities. 1 Overview Deep learning methods are a type of machine learning that utilizes neural network models for advanced pattern recognition and automatic feature … Read more
Follow our public account to discover the beauty of CV technology 1 Introduction Due to its superior capability for modeling global dependencies, the Transformer and its variants have become the primary architecture for many visual and language tasks. However, tasks like Visual Question Answering (VQA) and Referencing Expression Comprehension (REC) often require multi-modal predictions that … Read more
Paper Title: Inverted Transformers are Effective for Time Series Forecasting Authors: Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long Compiler: Sienna Reviewer: Los Introduction: iTransformer is the latest research achievement in the field of time series forecasting in 2024, currently demonstrating the best performance (SOTA) on standard time series … Read more
Source: Time Series Research This article is approximately 3400 words long and is recommended for a 5-minute read. This article introduces the Transformer in multivariate time series classification. Multivariate time series classification (MTSC) has attracted extensive research attention due to its diverse real-world applications. Recently, utilizing Transformers for MTSC has achieved state-of-the-art performance. However, existing … Read more