Publications by Selcuk Disci
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)...
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Backcast a Time Series for Covid-19 Truths
A couple of months ago, Turkey’s Health Minister announced that the positive cases showing no signs of illness were not included in the statistics. This statement made an earthquake effect in Turkey, and unfortunately, the articles about covid-19 I have wrote before came to nothing. The reason for this statement was the pressure of the Istanbul...
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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 the dollar and the Turkish lira. Let’s check ...
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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 in...
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Comparing the Coronavirus Pandemic (COVID-19) Management for some Developed Countries
The pandemic continues at full speed in Turkey where I live, because the government doesn’t conduct the process well; the data they provide is so doubtful, and the decisions they made are very inconsistent. So, I wondered about the situation in the other part of the world, especially the developed countries. In order to that, we will compare th...
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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 neural network. Neural Network Structure The neura...
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Feature Importance in Random Forest
The Turkish president thinks that high interest rates cause inflation, contrary to the traditional economic approach. For this reason, he dismissed two central bank chiefs within a year. And yes, unfortunately, the central bank officials have limited independence doing their job in Turkey contrary to the rest of the world. In order to check that ...
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Plotly for Data Visualization: The Vaccinations Effect on Covid-19
One of the most frequently asked questions these days is whether the vaccine works in reducing the number of cases and deaths. In order to examine that we will build a function so that we can run for every country we want without repeating the same code block. The dataset we’re going to use for this article is from Our World in Data. What we wa...
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Wildfires Comparison with ggplot2 dual Y-axis and Forecasting with KNN
In recent days, Turkey has been escalated wildfires. The government and the people were In recent days, Turkey has been escalated wildfires. The government and the people were on the alert and mobilized to put out the fires but, they couldn’t for days. A significant part of people thought that the simultaneous fires had no make sense, and there...
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ARIMA Method 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...
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