Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
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Updated
Mar 16, 2024
Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
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}
Bayesian Estimation of Structural Vector Autoregressive 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
State-Dependent Empirical Analysis: tools for state-dependent forecasts, impulse response functions, historical decomposition, and forecast error variance decomposition.
Regularized estimation of high-dimensional FAVAR models
Elastic-net VARMA: hyperparameter optimisation, estimation and forecasting
Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series
Utilized sentiment-based features to predict cryptocurrency returns, models used: Random Forest Classifier, Random Forest Regressor, and VAR time-series model
Forecasting exchange rates by using commodities prices
Unemployment Rate forecasting tool built for BMWi during the Data Science for Social Good Fellowship https://dssgxuk.github.io/bmwi/
Beer national sales forecasting
Cambridge UK temperature forecast python notebooks
Julia implementation of multi-variate time series models, such as vector autoregressive (VAR) and vector error correction (VECM) models.
An R package to model BVHAR
Code to reproduce paper Adrian, Duarte and Iyer (2023), “The Market Price of Risk and Macro-Financial Dynamics”
Personal repository for hobby and work projects
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