The Falling of ARK Innovation ETF: Forecasting with Boosted ARIMA Regression Model

During the pandemic, the stock prices almost doubled, but their trends have recently declined. One of the reasons for that might be the interest rates. To examine this, we will take a consideration ARK Innovation ETF (ARKK), which is a long-term growth capital by investing mostly in tech companies. First, we will create our datasets.Continue reading “The Falling of ARK Innovation ETF: Forecasting with Boosted ARIMA Regression Model”

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?”

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”