simulation

JAGS Simulation with Multivariate State-Space Model: The G7 on Food Security

The 49th G7 summit was held recently in Japan. Ukraine was one of the most critical issues at the meeting; most of the session topic was related to problems stemming from Russia’s invasion of Ukraine. One of the problems, aforementioned is food security. Because of the war, energy prices have been up. And that has stimulated food inflation. Of course,Continue reading “JAGS Simulation with Multivariate State-Space Model: The G7 on Food Security”

Military Expenditure Estimation with RJAGS Simulation

The side effect of the ongoing Russian occupation was that it stimulated countries’ intentions to increase their military budgets. Last month following the invasion, Germany announced a budget of 100 billion € for the restructuring of the army and announced that it would spend %2 of its GDP every year. Of course, it is notContinue reading “Military Expenditure Estimation with RJAGS Simulation”

Meta-Learning: Boosting and Bagging for Time Series Forecasting

I am always struggled to model the changes in gasoline prices as a categorical variable, especially in a small amount of time-series data. The answer to improving the performance of modeling such a dataset can be to combine more than one model. This method of combining and aggregating the predictions of multiple models is calledContinue reading “Meta-Learning: Boosting and Bagging for Time Series Forecasting”

Simulated Neural Network with Bootstrapping Time Series Data

In the previous article, we examined the performances of covid-19 management of some developed countries and we found that the UK was slightly better than others. This time we are going to predict the spread of disease for about a month in the UK. The algorithm we will use for this purpose is the neuralContinue reading “Simulated Neural Network with Bootstrapping Time Series Data”

Bootstrapping Time Series for Gold Rush

Bootstrap aggregating (bagging), is a very useful averaging method to improve accuracy and avoids overfitting, in modeling the time series. It also helps stability so that we don’t have to do Box-Cox transformation to the data. Modeling time series data is difficult because the data are autocorrelated. In this case, moving block bootstrap (MBB) shouldContinue reading “Bootstrapping Time Series for Gold Rush”