Projetos de modelagem e previsão de séries temporal em linguagem Python e linguagem R. Usarei vários modelos de bibliotecas e pacotes usados para tratamento, modelagem e previsão de séries temporais. Falarei um pouco sobre cada uma delas, gerarei a validação e as previsões e, por fim, realizarei a avaliação com a métricas pertinentes.

Stock market prediction on 5 italian companies using VAR model, OLS regressions and LSTM recurrent neural networks over data retrieved from Refinitiv Eikon

This repository contains a collection of time series analysis and forecasting projects, featuring both classical statistical models and deep learning approaches.

This project investigates the monetary transmission mechanism along with IS-LM Curve and the short-run Phillips Curve in Japan over the period 2000–2025

SMU ECON248 "Financial Market and Monetary Policy" project. A Vector Autoregression (VAR) model was used to evaluate the use of exchange rate as monetary policy to achieve Economic Growth and Price Stability in Vietnam. Data obtained from SEIC database.

MSc thesis about sales forecasting in high-dimensional contexts taking into account product relationships and promotions

Empirical investigation of the Jevons Paradox — testing whether hardware cost declines Granger-cause software investment growth (VAR/VECM)

Detailed implementation of various time series analysis models and concepts on real datasets.

Codebase for Term Paper of course ECON F244: Economics of Growth and Development at BITS Pilani, Pilani campus (Spring '21)

Dynamic Financial Markets is a data-driven project analyzing stock interactions in the Indian cement sector. Using models like GARCH, Johansen Cointegration, and VAR, it explores volatility, long-term relationships, and dynamic dependencies among Shree Cement, UltraTech, and Ambuja Cement.

Vector Autoregression model for Egyptian macroeconomic forecasting

Masters Thesis Project on Media Prejudice

Quantitative analysis of Russia's structural dependency on China post-2022 sanctions : VAR, Event Study, Network Analysis & Trade Econometrics (Python)

Our Healthcare Inventory Forecasting solution uses AI-driven models to optimize inventory, reduce stockouts, and prevent overstocking in healthcare. It integrates data from sales and market trends, using ARIMA and VAR models for precise demand forecasting. With automation from OpenAI, Power BI visualizations, and chatbot support, the system enhanc

Examining high-frequency ETF data using ARMA, VAR, and GARCH models to capture return dynamics and volatility.

Modelling data using the VaR

Forecasting COVID-19 Cases

The Time Series Analysis repository contains various models and techniques used for analyzing time-dependent data.

30-day Bitcoin price forecasting using ARIMA & VAR models with 95% confidence intervals and Granger causality analysis (EUR/USD, Gold, S&P 500, Crude Oil)

A comprehensive collection of time series analysis notebooks covering statistical modeling & forecasting.

Generate time-series data using VAR model parameters (AI@UW engr-stocks group)

Empirical study examining the response of labor supply to technology shocks, contrasting the opposing findings of Gali (1999) and Christiano et al. (2003). Using data from the FREDII database, this analysis computes log labor productivity and per-capita hours for the nonfarm business sector

Econometric analysis of consumer confidence dynamics using a VAR model in Python.

Estimação de modelos VAR(3) e VAR(10) para 91 países no período de 1960 a 2019, a fim de testar a causalidade de Granger entre as variáveis de Poupança Interna Bruta e crescimento do Produto Interno Bruto per capita.