{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for multivariate modeling and forecasting

Spatiotemporal datasets collected for network science, deep learning and general machine learning research.

Bayesian Estimation of Structural Vector Autoregressive Models

Time Series Forecasting for the M5 Competition

Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}

Functions for Bayesian inference of vector autoregressive and vector error correction models

Remaining useful life estimation of NASA turbofan jet engines using data driven approaches which include regression models, LSTM neural networks and hybrid model which is combination of VAR with LSTM

Sentiment analysis of Reddit comments to predict bitcoin price movement

Elastic-net VARMA: hyperparameter optimisation, estimation and forecasting

State-Dependent Empirical Analysis: tools for state-dependent forecasts, impulse response functions, historical decomposition, and forecast error variance decomposition.

Utilized sentiment-based features to predict cryptocurrency returns, models used: Random Forest Classifier, Random Forest Regressor, and VAR time-series model

Bayesian SVARs with Sign, Zero, and Narrative Restrictions

Forecasting exchange rates by using commodities prices

Regularized estimation of high-dimensional FAVAR models

Cambridge UK temperature forecast python notebooks

Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series

Unemployment Rate forecasting tool built for BMWi during the Data Science for Social Good Fellowship https://dssgxuk.github.io/bmwi/

Beer national sales forecasting

R and Python package to model Bayesian VAR and VHAR models

Toolkit functions and example outputs for Bayesian (Structural) Vector Autoregressive (VAR) models