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)

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

Time Series Analysis using LSTM for Wind Energy Prediction.

Time Series Analysis of Air Pollutants(PM2.5) using LSTM model

Time series Analysis using LSTM,RNN and GRU with pytorch

Using LSTM network for time series forecasting

Analysis of various deep learning based models for financial time series data using convolutions, recurrent neural networks (lstm), dilated convolutions and residual learning

Compare how ANNs, RNNs, LSTMs, and LSTMs with attention perform on time-series analysis

LSTM Time Series Analysis using Tensorflow

Multivariate Time series Analysis Using LSTM & ARIMA

Time series prediction and text analysis using Keras LSTM, plus clustering, association rules mining

I predict air quality index of a city in China using a Long Short Term Memory (LSTM) neural network. for a year. Executed time series analysis

This project implements a time series multivariate analysis using RNN/LSTM for stock price predictions. A deep RNN model was created and trained on five years of historical Google stock price data to forecast the stock performance over a two-month period.

The research provides effective management strategies for different asset portfolios in the financial sector by building models. The VMD-LSTM-PSO model is developed for daily financial market price forecasting, where the time series are decomposed by VMD and the sub-series are used as LSTM input units to carry out forecasting, and then the network parameters are adjusted by PSO to improve the forecasting accuracy, and the Huber-loss of the model is 1.0481e-04. For the daily portfolio strategy, EEG is used to construct a system of investment risk indicators, which is optimized by incorporating the risk indicators into the Sharpe index, and the objective function is analyzed by using GA to derive the optimal daily asset share that maximizes the investor's return with minimal risk. The results of the empirical analysis show that the model provides strategies with good robustness.

Time Series Forecasting Problem

Analysis of Time Series data using Seq2Seq LSTM and 2 attention layers

Classification & Time Series Analysis of Spotify Data using Grid Searched Random Forest & LSTM.

In this repository, based on the latest NLP pappers, we researched on sequential data and time series and developed tasks in NLP such as stock price prediction, time series prediction, sentiment analysis from text and We developed the language model and so on. This research is based on recurrent neural networks, LSTM networks and the new Transformer architecture and attention mechanism.

Financial time series analysis and prediction have become an important area of re- search in today's world. Designing and pricing securities, construction of portfolios and other risk management strategies depends on the prediction of financial time se- ries. A financial time series often involve large dataset with complex interaction among themselves. A proper analysis of this data will give the investor better gains, but the existing methodologies focus on linear models (AR, MA, ARMA, ARIMA) and non- linear models (ARCH, GARCH, TAR). These models are not capable of identifying the complex interactions and latent dynamics existing within the data. Applying Deep learning methods to these types of data will give more accurate results than the existing methods. Deep learning architectures can identify the hidden patterns in the data and is also capable of exploiting the interactions existing within the data, which is, at least not possible by the existing financial models. The proposed work uses four different deep learning architectures (RNN, LSTM, CNN, and MLP) for predicting the minute wise stock price for NSE listed companies and compares the performance of the mod- els. The proposed method uses a sliding window based approach for predicting future values on a short-term basis. The performance of the models was quantified using error percentage.

Extensive time series analysis of chinese PM2.5 content, using models from ARMA and VAR to LSTMs and dynamic time warping clustering

Detect future values of stock prices of a set of companies using time series analysis — RNN, LSTM & GRU with 95% accuracy.

Analysis of Air Pollution prediction and time-series forecast of PM2.5 Pollutant using Machine Learning Algorithms (SVM, Decision Tree and Random Forest) and Deep Learning Algorithms (CNN, Bi-LSTM). Also considered for improved performances is random search hyper-parameter tuning using Ray-Tune with HyperBand Scheduler strategy.

[Python] Adopted Neural Network (LSTM), Time Series (ARIMA) and Sentiment Analysis (NLP) to predict Bitcoin prices

Predicted the Price of the Cryptocurrency(Bitcoin) using the past time-series data, Twitter Sentiments(Polarity and Sensitivity), Currency's Fundamentals and Technical Indicators like RSI and SMA on LSTM. The Notebook contains the Exploratory data analysis(with important links) and the astounding result at the end of it

This work implements RNN and LSTM models using Python and MATLAB for temperature forecasting, covering setup, data preprocessing, model training, and evaluation with metrics like MAE and RMSE. It employs time series analysis and statistical assessment techniques, providing visualizations to demonstrate model accuracy and practical application.

It is challenging to build useful forecasts for sparse demand products. If the forecast is lower than the actual demand, it can lead to poor assortment and replenishment decisions, and customers will not be able to get the products they want when they need them. If the forecast is higher than the actual demand, the unsold products will occupy inventory shelves, and if the products are perishable, they will have to be liquidated at low costs to prevent spoilage. The overall objective of the model is to use the retail data which provides us with historic sales across various countries and products for a firm. We use this information given, and make use of FM’ s to predict the sparse demand with missing transactions. The above step then enhances the overall demand forecast achieved with LSTM analysis. As part of the this project we answered the following questions: How well does matrix factorization perform at predicting intermittent demand How does matrix factorization approach improve the overall time-series forecasting

The basic idea of the project is to perform various data and time-series analysis on the live, present and past records of the stocks. This data driven approach will help to manage portfolio of an indivdual and will eventually increase the profits margin. Here, we will be importing data from yfinance library. And the data have been considered from the year 2010. The yfinance dataset consists of the columns such as Date, Open, High, Low, Close, Volume, and Dividend. During the flow of this notebook, you will see stock analysis, time series analysis, time series model prediction with the help of FB Prophet, LSTM, ARIMA, SARIMA and XGBoost.

Arima/DNN/LSTM ... based Time Series Analysis projects

LSTM based Time Series Analysis for Rainfall Prediction