{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for time series analysis and forecasting
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Updated
Sep 6, 2024 - R
{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for time series analysis and forecasting
Time Series Forecasting for the M5 Competition
Functions for Bayesian inference of vector autoregressive and vector error correction models
Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}
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
Bayesian Estimation of Structural Vector Autoregressive Models
Regularized estimation of high-dimensional FAVAR models
Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
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/
Sentiment analysis of Reddit comments to predict bitcoin price movement
State-Dependent Empirical Analysis: tools for state-dependent forecasts, impulse response functions, historical decomposition, and forecast error variance decomposition.
Elastic-net VARMA: hyperparameter optimisation, estimation and forecasting
Cambridge UK temperature forecast python notebooks
Beer national sales forecasting
Code to reproduce paper Adrian, Duarte and Iyer (2023), “The Market Price of Risk and Macro-Financial Dynamics”
R Time series packages not included in CRAN Task View: Time Series Analysis
Bayesian SVARs with Sign, Zero, and Narrative Restrictions
Toolkit functions and example outputs for Bayesian (Structural) Vector Autoregressive (VAR) models
Forecasting exchange rates by using commodities prices
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