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Continue reading →: Dynamic Regression (ARIMA) vs. XGBoostIn 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…
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Continue reading →: Time Series Forecasting with XGBoost and Feature ImportanceThose 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…
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Continue reading →: Backcast a Time Series for COVID-19 TruthsA 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…
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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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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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