Added by Selcuk Disci on January 29, 2021 at 12:00am — No Comments
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) should be preferred because MBB resamples the data inside overlapping blocks to…Continue
Added by Selcuk Disci on December 14, 2020 at 5:58am — No Comments
Added by Selcuk Disci on October 24, 2020 at 11:00pm — No Comments
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, autoregressive integrated moving average (ARIMA).
We will use the monthly prices of refined gold futures(XAUTRY) for one gram…Continue
Added by Selcuk Disci on September 30, 2020 at 10:00am — No Comments
In my previous article, we analyzed the COVID-19 data of Turkey and selected the cubic model for predicting the spread of disease. In this article, we will show in detail why we selected the cubic model for prediction and see whether our decision was right or not.
When we analyze the regression trend models we should consider overfitting and underfitting…Continue
Added by Selcuk Disci on July 10, 2020 at 2:30am — No Comments
Added by Selcuk Disci on May 13, 2020 at 5:18am — No Comments