The practitioner's forecasting library
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.
In this notebook, we will create an AI and time serie driven forecasting engine based on a set of 5 AI models and 5 time series models and employ several algorithms to perform feature engineering and selection on a multivariate time series dataset.
R package for sparse VAR estimation
Machine learning applications in Finance.
Supporting code for UvA Masters of Quantitative Finance thesis in CDS/Bonds arbitrage trading
An R package to implement VEC models
Simplistic tool for item price prediction in Hypixel Skyblock using machine learning, neural network and more.
Python library for financial time series analysis and algorithmic trading. VECM-GARCH models, multivariate analysis, and trading strategies. Includes Udemy course materials.
ARDL cointegration in Python: three-test bounds testing with degeneracy classification, response-surface critical values, bootstrap inference, NARDL, QARDL, Fourier-ADL, and heterogeneous panels (MG/PMG/CS-ARDL). All was validated against R and Stata.
Forecasting financial instrument prices using Vector Error Correction Models (VECM) and VAR. Includes cointegration analysis, impulse response functions, and forecast diagnostics for robust time series modeling.
Топ-10 конкурса ВКР Банка России, РЭШ и НИУ ВШЭ (2026): моделирование и walk-forward прогнозирование кривой доходности ОФЗ (Nelson-Siegel, VECM, Random Forest, CatBoost)
The code lets you create, plot, estimate Vector Error Correction Models on FANG stocks.
VAR or VECM for fishery capture (2002-2015)
Key Factors Affecting Oil Prices and the Market Share of ICE, HEV, and EV in Malaysia
Time Series exercises closely following: Enders, W. (2014) Applied Econometric Time Series and Lutkepohl (2017), Structural Vector Autoregressive Analysis
Time-series econometric analysis of the export–growth nexus in Japan using VECM, Granger causality, and IRF.
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