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Continue reading →: Bootstrapping Time Series for Gold RushBootstrap 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,…
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Continue reading →: Approaches to Time Series Data with Weak Seasonality: Dynamic Harmonic RegressionIn 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,…
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Continue reading →: Time Series Forecasting: KNN vs. ARIMAIt 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…
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Continue reading →: Model Selection: Adjusted Coefficient of Determination-Variance TradeoffWARNING: IN THE INTERVIEW AT 25/11/2020, THE MINISTER OF HEALTH OF THE REPUBLIC OF TURKEY CONFESSED THAT THE PEOPLE WHOSE TESTS(COVID-19) WERE POSITIVE BUT DID NOT SHOW THE SYMPTOMS, HAD NOT BEEN INCLUDED IN THE DAILY NEW CASES NUMBERS UNTIL NOW; IN THIS CASE THE DATASOURCE WE USED FOR TURKEY…
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I’m Selcuk Disci
The DataGeeek focuses on machine learning, deep learning, and Generative AI in data science using financial data for educational and informational purposes.
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