Time series Analysis using LSTM,RNN and GRU with pytorch

Anomaly Detection in Time Series Data using Recurrent Neural Networks (RNNs), including Simple RNN, LSTM, and GRU models, as well as statistical methods like Z-score analysis

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Deep learning time-series analysis of financial market data using RNN, LSTM, CNN, GRU, and Bi-GRU models to study temporal patterns and forecasting behavior.

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Time Series Forecasting and Sentiment analysis using RNN, LSTMs (vanilla, bidirectional and stacked) and GRUs.)

This repository covers essential techniques for time series analysis and forecasting. It covers data manipulation and visualization using Numpy and Pandas, time series analysis with Statsmodels, ARIMA models, deep learning methods like RNNs, LSTM, GRU, etc. and Facebook's Prophet library.

A deep learning-based time series analysis for predicting Bitcoin prices using RNN, LSTM, and GRU models. The project explores historical BTC data, visualizes trends, and compares model performance for accurate future price forecasting.

This project implements deep learning models (LSTM, RNN, GRU) in PyTorch for time series forecasting of air quality in Madrid using the Kaggle dataset (2001-2018). It includes data preprocessing, model training/evaluation, and a comparative study of model efficacy. Features clustering analysis for spatial insights.

Determining the stock trade information is a vital money-related subject that includes an assumption that the basic data freely accessible within the past has a few prescient connections to long-run stock returns. Stock market estimating involves revealing the showcase patterns, arranging speculation strategies, recognizing the finest time to buy the stocks and which stocks to buy. Time-series data analysis methods utilize irrefutable data as the premise for assessing future results. Time series information can be characterized as numerical information collected in a specific grouping over a period at customary intervals. The purpose is to discover in case there's an interface between the information collected so far and in what way does the information change. In this project, we performed stock market forecasting using time series analysis with the help of recurrent neural network models(RNNs) and a classical model to compare and analyze the accuracy of various models. We implemented long short-term memory(LSTM), Gated recurrent units(GRU) and the classical model Autoregressive integrated moving average(ARIMA) to predict stock prices. We also aimed to perform risk analysis on each of the stocks we used in the project.