My Blog

Update : 2018-05-06 I have found that the website structure of Indeed, Monster, Dice, Careerbuilder have been changed since I wrote these posts. So, some of my code may not work now. But, I think the concept and the process is still the same. The Github repository for this project : choux130/webscraping_example. From the previous posts, How Web Scraping eases my job searching pain? - Part I : Scrape contents from one URL and How Web Scraping eases my job searching pain?

Update : 2018-05-06 I have found that the website structure of Indeed, Monster, Dice, Careerbuilder have been changed since I wrote these posts. So, some of my code may not work now. But, I think the concept and the process is still the same. The Github repository for this project : choux130/webscraping_example. From the previous post, How Web Scraping eases my job searching pain? - Part I : Scrape contents from one URL, we know how to summarize all the job searching results from Careerbuilder into a .

Update : 2018-05-06 I have found that the website structure of Indeed, Monster, Dice, Careerbuilder have been changed since I wrote these posts. So, some of my code may not work now. But, I think the concept and the process is still the same. The Github repository for this project : choux130/webscraping_example. The process of finding jobs online can definitely be a torture. Every day is started by sitting in front of computer, browsing different kind of job searching websites and trying to track every job I have read and then categorizing them into interested or not interested.

The Github repository for this website : choux130/slide_thesis_ioslides. The link to my slides : https://choux130.github.io/slide_thesis_ioslides/#1 Having an opportunity to give a presentation for my master thesis, I decided to give it a try on R Markdown Presentation with interactive graphs and planned to publish it online after the presentation. There are three main choices in R Studio for the R Markdown Presentation: ioslides, Slidy, and Beamer. Beamer is for .

BATS model is Exponential Smoothing Method + Box-Cox Transformation + ARMA model for residuals. The Box-Cox Transformation here is for dealing with non-linear data and ARMA model for residuals can de-correlated the time series data. Alysha M.(2010) has proved that BATS model can improve the prediction performance compared to the simple Sate Space Model. However, BATS model does not do well when the the seaonality is complex and high frequency.

The main weakness of original Exponential Smoothing Method is that it can only provide point estimation. Hyndman (2002) proposed using state space framework to rewrite the original exponential smoothing algorithm and then give distribution assumption on the error terms to calculate the prediction interval. There are two types of error terms in the state space model: Additive and Multiplicative. The point estimator for these two models are the same but the prediction intervals are different.