Food Crisis Analysis and, Forecasting with Neural Network Autoregression

The war between Russia and Ukraine has affected the global food supply other than many vital things. Primarily cereal crop products have been affected the most because the imports have been provided to the world mainly through Ukraine and Russia. Let’s check the situation we’ve mentioned for G20 countries. We will get a look atContinue reading “Food Crisis Analysis and, Forecasting with Neural Network Autoregression”

Nuclear Threat Projection with Neural Network Time Series Forecasting

Unfortunately, we have been through tough times recently as going on Russian invasion in Ukraine. As Putin stacked to the corner via sanctions and lost in the field, he has been getting to be more dangerous. He has even threatened to use nuclear weapons if necessary. Because of the nuclear danger we’ve just mentioned above,Continue reading “Nuclear Threat Projection with Neural Network Time Series Forecasting”

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”

Lagged Predictors in Regression Models and Improving by Bootstrapping and Bagging

Huge lines at gas stations have been seen around Turkey in recent days. Under the reason for this is the rise in the prices so often, in the last few months and the expectation to continue this. Of course, it is known that the rise in the exchange rate (US dollar to Turkish lira) hasContinue reading “Lagged Predictors in Regression Models and Improving by Bootstrapping and Bagging”

Dynamic Regression with ARIMA Errors: The Students on the Streets

The higher education students have had trouble being housing in Turkey in recent days. There have been people who even sleep on the streets like a homeless. The government has been accused of investing inadequate dormitories for sheltering the students. Let’s examine the ongoing sheltering problem of students. The dataset we have built for thisContinue reading “Dynamic Regression with ARIMA Errors: The Students on the Streets”

Forecasting with ARIMA from {fable}: The Election is Coming for Turkey?

Nowadays, every journalist and intellectual talks about a probable early election in Turkey’s ongoing poor economic conditions. But, is it politically right decision to go early election before the officially announced 23 June 2023 in terms of ruling parties? In order to answer this question, we have to choose some variables to monitor economic conditions,Continue reading “Forecasting with ARIMA from {fable}: The Election is Coming for Turkey?”

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”

Dynamic Regression (ARIMA) vs. XGBoost

In the previous article, we mentioned that we were going to compare dynamic regression with ARIMA errors and the xgboost. Before doing that, let’s talk about dynamic regression. Time series modeling, most of the time, uses past observations as predictor variables. But sometimes, we need external variables that affect the target variables. To include thoseContinue reading “Dynamic Regression (ARIMA) vs. XGBoost”

Time Series Forecasting with XGBoost and Feature Importance

Those who follow my articles know that trying to predict gold prices has become an obsession for me these days. And I am also wondering which factors affect the prices. For the gold prices per gram in Turkey, are told that two factors determine the results: USA prices per ounce and exchange rate for theContinue reading “Time Series Forecasting with XGBoost and Feature Importance”

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”