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”

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”