This project aims to predict VOLATILITY S&P 500 (^VIX) time series using LSTM.

在企业场景中，对订单或者发货的预测，通常是一个超级难度的不可控事件；如果依靠利用经典概率算法的深度学习神经网络，那么特征向量猜想及数据收集工作在实际落地过程中将成为不可能；而通过采用仅仅依靠订单或者发货的时间序列数据，通过有状态的LSTM递归神经网络，通过调整时间窗口来训练数据，可以做到数据的回归预测。In the enterprise scenario, the forecast of orders or shipments is usually an uncontrollable event of super difficulty; if relying on deep learning neural networks using classical probability algorithms, the feature vector guessing and data collection work will become Impossible; and by using time-series data that relies solely on orders or shipments, through adopt stateful LSTM recurrent neural network, by adjusting the time window to train the data, regression prediction of the data can be achieved.

The energy sector is one of the largest and most important sectors out there. The ability to efficiently forecast hourly energy consumption plays an important role on how energy is distributed and consumed. Deep learning algorithms have played vital roles in prediction and forecasting problems alike. In this example, the deep learning algorithm technique known as Recurrent Neural Networks (RNN) and Long-Term Short Memory (LSTM) are applied on a time series data set consisting of hourly energy consumption for different counties according to their clients and activities with the aim of making forecast on future energy consumption. Models generally performed better by reducing batch size and by increasing epoch sizes. Having evaluated the results using RMSE, MAE and R2 scores, the LSTM and RNN models are both seen to have excellent performances in the forecasting of hourly energy consumption.

Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. ... LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series.

Deep Learning Model for Time Series Forecasting of Air Quality using LSTM (Long Short-Term Memory), a type of recurrent neural network suited for sequence prediction

The project aims to develop and evaluate various deep learning models for time series forecasting using historical stock price data of the Apple (AAPL) stock. It employs Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) models, and a Multivariate LSTM model to make predictions.

This project builds a stock price prediction system using Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN) designed for time-series forecasting. It utilizes Yahoo Finance (yfinance) for stock data, TensorFlow/Keras for deep learning, and Streamlit for an interactive web-based interface.

In this project, I have used the deep learning knowledge of recurrent neural network (e.g. LSTM, GRU, etc.) and feed forward neural network (dense layer), to perform time series forecasting for WTI crude oil prices. And from the prediction given by the models, to provide business insights for the crude oil industry.

In this project, I will use the deep learning knowledge of recurrent neural network (e.g. LSTM, GRU, etc.) and feed forward neural network (dense layer), to perform time series forecasting for WTI crude oil prices. And from the prediction given by the models, to provide business insights for the crude oil industry.

Photovoltaic power generation has always been a research hotspot in the field of new energy. However, as the demand for energy efficiency has increased, the sustainable development of the photovoltaic industry has encountered great challenge. The power grid has refused to connect large-scale photovoltaic power generation into the grid, and the phenomenon of “abandoning light” has become increasingly serious. These force us to carry out accurate PV forecasting, adopt more accurate scheduling decisions, and achieve multi-energy coordinated control, so that intermittent power supply grids have technical support. Traditional PV forecasting methods have gradually lost their advantages in the face of increasing PV data. The physical method requires an accurate prediction model and a large number of empirical coefficients. The process is cumbersome and the error is large. The statistical method requires a large amount of historical operational data, which has high requirements for the optimization of the computational model. In view of this, this paper adopts a framework based on deep learning; considering that the influence factors of PV prediction are physical quantities that change with time, and the recurrent neural network (RNN) algorithm has strong processing ability for time series; The feature set constitutes a training set, and the most important structure in RNN——long-short-term memory network (LSTM), is used to predict photovoltaic power generation. LSTM has the functions of “forget” and “update”, which solves the problem of long-order dependency, so that the feature can be well preserved in subsequent calculations without memory dissipation. The results show that the PV prediction based on the LSTM model has a qualitative change in the prediction accuracy, and the prediction speed is also significantly improved.

This project demonstrates the application of deep learning techniques to solve three different problems: Binary classification of tabular data ,Image classification using Convolutional Neural Networks (CNN),Time-series prediction using Recurrent Neural Networks (RNN with LSTM)

Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are powerful deep learning architectures used for sequential data processing. Both LSTM and RNN models excel in tasks such as time series prediction, natural language processing, and sequence generation.

This project implements stock market prediction models using Recurrent Neural Networks (RNN) and LSTM in Python. It applies deep learning techniques for time-series forecasting, analyzing historical stock data to predict future price trends. The project demonstrates model building, evaluation, and visualization of stock predictions.

The Stock Price Trend Prediction with LSTM project aims to predict future stock market trends by analyzing historical stock price data using deep learning techniques. It utilizes Long Short-Term Memory (LSTM) networks — a type of Recurrent Neural Network (RNN) that excels at capturing long-term dependencies in time-series data.

This repository presents a time-series forecasting framework for predicting temperature using deep learning models, including LSTM, SimpleRNN, and GRU networks. The study is based on historical weather data and focuses on comparing the performance of different recurrent neural network architectures for short-term temperature prediction.

Stock price prediction using Long Short-Term Memory (LSTM) is a deep learning approach designed to model and forecast time-series data such as financial markets. LSTM is a specialized type of recurrent neural network (RNN) that can learn long-term dependencies by maintaining an internal memory through structures.

This repository contains the files for the time series data augmentation models such as the traditional statistical ARIMA model, the deep learning model WaveNet and the recurrent neural network LSTM. The models are used to prediction temperature datasets with or without data augmentation techniques.

In this project, I have used the deep learning knowledge of recurrent neural network (e.g. LSTM, GRU, etc.) and feed forward neural network (dense layer), to perform time series forecasting for WTI crude oil prices. And from the prediction given by the models, to provide business insights for the crude oil industry.