vectorautoregression

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

Vector Autoregression augmented with deep learning.

MCMC estimation of Bayesian Vectorautoregressions

Filters (kalman, hodrick-prescott, moving average) together with comparison and sensitivity analysis (in notebook filters_with_parameters)+var analysis and granger causality test. Test for random walk (CE currencies using yfinance API)

Hybrid fuzzy model for financial time series forecasting

Early version of a personal package of mine for estimating Bayesian VAR models

Part of my PhD thesis - a framework for reconstruction of damaged AIS data, consisting of 3 stages: clustering, anomaly detection and prediction

A collection of assessments in Time Series Analysis completed as part of my Econometrics program.

Stochastic Processes Comparison of Accuracy: Data Assimilation, Echo State Machines and Nonlinear Vector Autoregressive Learning Methods. ### Comparing discrete and variational data assimilation methods to reservoir computing - machine learning for synthetic chaotic nonlinear dynamical systems

아주대학교 2021-2 비즈니스 애널리틱스 프로젝트

*Time Series Forcasting Project* :- We tested Fb prophet model , VAR (Vector Autoregression) Model , Transformer Models(Only Encoder) and LSTM's.

Data Science Capstone Project:

The VAR model is used to forecast the appliances energy on the previous usage history. The data were first tested using adfuller test, granger casuality test. The lag value of 7 was determined for VAR model after running it iteratively for values upto 48.

Non-linear topology identification using Deep Learning. Sparsity (lasso) is enforced in the sensor connections. The non-convex and non-differentiable function is solved using sub-gradient descent algorithm.

This study is based on confirmed cases and deaths collected from Pakistan. Results demonstrate the promising potential of TIME SERIES model in forecasting COVID-19 cases and highlight the superior performance of the time series compared to the LSTM.we apply AI-based forecasting models such time series ARIMA, LSTM, prophet and VAR.