Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

time series forecasting using pytorch，including ANN,RNN,LSTM,GRU and TSR-RNN，experimental code

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

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LSTM-XGBoost Time Series Forecasting

Building Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention. Additionally, hybrid models like GRU-XGBoost and LSTM-Attention-XGBoost for Electricity Demand and price prediction

time series forecasting using keras, inlcuding LSTM,RNN,MLP,GRU,SVR and multi-lag training and forecasting method, ICONIP2017 paper.

Time series forecasting using LSTM in Python

多元多步时间序列的LSTM模型预测——基于Keras

Perform multivariate time series forecasting using LSTM networks and DeepLIFT for interpretation

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This project forecasts renewable energy demand using LSTM-based time series models. It processes historical demand data, trains predictive models, and visualizes future trends, enabling better planning and management

Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convolutional neural networks, Multi-layer perceptron

A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series

Predicting future temperature using univariate and multivariate features using techniques like Moving window average and LSTM(single and multi step))

使用LSTM、GRU、BPNN进行时间序列预测。Using LSTM\GRU\BPNN for time series forecasting. (Pytorch Edition)

Using K-NN, SVM, Bayes, LSTM, and multi-variable LSTM models on time series forecasting

Time Series Forecasting with xLSTM

Financial Time Series Price forecast using Keras for Tensorflow. RNN LSTM

使用支持向量机、弹性网络、随机森林、LSTM、SARIMA等多种算法进行时间序列的回归预测，除此以外还采取了多种组合方法对以上算法输出的结果进行组合预测。Support vector machine, elastic network, random forest, LSTM, SARIMA and other algorithms are used for regression prediction of time series. In addition, a variety of combination methods are used to forecast the output of the above algorithms.

Using LSTM network for time series forecasting

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Optimised Extended LSTM for time-series forecasting

This project uses an LSTM neural network to predict air quality (PM2.5) from synthetic time-series data. It includes data generation, normalization, model training, and prediction visualization. The results demonstrate how deep learning can forecast pollution levels

Predicting the behavior of $BTC-USD by training a memory-based neural net on historical data

Time-Series forecasting using Stats models, LightGBM & LSTM

Time-series forecasting with 1D Conv model, RNN (LSTM) model and Transformer model. Comparison of long-term and short-term forecasts using synthetic timeseries. Sequence-to-sequence formulation.