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 forContinue reading “Plotly for Data Visualization: The Vaccinations Effect on Covid-19”

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 thatContinue reading “Feature Importance in Random Forest”

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”

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,Continue reading “Comparing the Coronavirus Pandemic (COVID-19) Management for some Developed Countries”

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”

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 theContinue reading “Backcast a Time Series for COVID-19 Truths”

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”

Approaches to Time Series Data with Weak Seasonality: Dynamic Harmonic Regression

In the previous article, we have tried to model the gold price in Turkey per gram. We will continue to do that to find the best fit for our data. When we chose the KNN and Arima model, we saw the traditional Arima model was much better than the KNN, which is a machine learningContinue reading “Approaches to Time Series Data with Weak Seasonality: Dynamic Harmonic Regression”

Time Series Forecasting: KNN vs. ARIMA

It is always hard to find a proper model to forecast time series data. One of the reasons is that models that use time-series data often expose to serial correlation. In this article, we will compare k nearest neighbor (KNN) regression which is a supervised machine learning method, with a more classical and stochastic process,Continue reading “Time Series Forecasting: KNN vs. ARIMA”