ARCH models in Python

Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

Econometric Analysis of Explosive Time Series

Bitcoin price prediction using ARIMA Model.

Pair Trading Analysis & Exercises Toolkit [Jupyter Notebook]

Stationarity check using the Augmented Dickey-Fuller test from Scratch in Python

R Package for Bootstrap Unit Root Tests

Can a Long Short-Term Memory Model Produce Accurate Stock Price Predictions?: A Deep Learning Approach to Predicting Apple Inc. Stock Price.

Forecast airline passenger demand using time series models like AR, ARMA, and LSTM to improve operations, optimize scheduling, enhance resource allocation, and streamline supply chain management through accurate demand predictions

Implementation of LSTM time series tuned with GRU.

Impact of macroecomonic variables on S&P 500

ARIMA and GARCH modelling

Time Series Analysis

Time Series Forecasting using ARIMA

MATLAB implementation of the DF-GLS unit root test of Elliott, Rothenberg & Stock (1996), with 3 optimal lag-length selection methods (SIC, MAIC, Sequential-t) for selecting the lagged terms in the underlying ADF regression.

This folder contains all the machine learning projects like numbers, text, timeseries etc.

Modelo de machine learning con series temporales que predice la cantidad de taxis para la próxima hora.

SmoothTrend is a comprehensive time series analysis tool that utilizes Holt-Winters, Holt, and Simple Exponential Smoothing methods, as well as ARIMA/SARIMA modeling, to perform advanced trend analysis, stationarity testing, residual analysis, and forecasting.

Data Visualization and Predictive model using Python

Laboratorio numero 3 de la clase Data Science para predecir series de tiempo en predicción de importaciones de gasolina