Thiyanga S. Talagala

Monash University, Australia

I am a Senior Lecturer in the Department of Statistics, Faculty of Applied Sciences at the University of Sri Jayewardenepura. I received my PhD in statistics from Monash University, Australia in 2019. My thesis advisors were Professor Rob J Hyndman and Professor George Athanasopoulos.

I am also an Associate Investigator of the Australian Research Council (ARC) Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).

I enjoy solving general data science problems from three different angles: theoretical, computational, applied. On this website you will find some of my work and interests in statistics and data analysis. My research focuses on developing new statistical machine learning tools to help both practitioners and theoreticians make more open, explainable and reproducible data-driven discoveries. I am also interested in R programming language.

Priyanga Dilini Talagala PhD in Statistics, Monash University, Australia is my sister.

Interests

• Time Series Analysis
• Data Visualization
• Computational Statistics
• Machine Learning
• Machine Learning Interpretability
• Applied Statistics
• Algorithm Selection

Education

• PhD in Statistics, 2019

Monash University, Australia

• MSc in Financial Mathematics, 2015

University of Moratuwa, Sri Lanka

• BSc (Hons) Special Degree in Statistics, 2012

University of Sri Jayewardenepura, Sri Lanka

• Professor R A Dayananda Gold Medalist and Batch first, 2012

Data Import

tea: R package for tea exporting countries

mozzie: R package for dengue cases in Sri Lanka

colmozzie: R package for dengue cases and climate variables in Colombo Sri Lanka

m4comp2018: R package for M4 Competition time series data

DSjobtracker: R package containing information related to data science job advertisements. What skills and qualifications are required for data science related jobs?

Projects

Teaching Statistics

Data sets for teaching data analysis.

R-Ladies is a worldwide organization whose mission is to promote diversity in the R community.

Large-Scale Time Series Forecasting

Computationally efficient forecasting methods for large-scale real-time applications

Programming and Data Analysis with R

The course website for my teaching unit STA 326 2.0 Programming and Data Analysis with R

Small Bite Big threat

Potential impacts of climate change on dengue fever

Talks

A Tool to Detect Potential Data Leaks in Forecasting Competitions

Abstract A Tool to Detect Potential Data Leaks in Forecasting Competitions Forecasting competitions are of increasing importance as a …

Feature-based Time Series Forecasting

Abstract This work presents two feature-based forecasting algorithms for large-scale time series forecasting. The algorithms involve …

Peeking inside FFORMS: Feature-based FORecast Model Selection

Abstract Peeking inside FFORMS: Feature-based FORecast Model Selection Thiyanga S. Talagala$^1$, Rob J. Hyndman$^1$, George …

Feature-based time series forecasting

Abstract This work presents three feature-based algorithms for large-scale time series forecasting. The algorithms are developed based …

Feature-based model selection for time series forecasting

Abstract Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is …

Posts

Let’s see how to format your model outputs with parameters package in R by Lüdecke et al. (2020). Fit a simple linear regression model …

Organize your priorities using CalendR package in R

Maintaining a calendar to organize your events allows for having more smooth and productive day. In this post, I will show you some …

Ratio matters: change the way you see things!

Aspect ratio: what it is and why it is. How often do you fix aspect ratio?

Highlight data points in a scatterplot

Highlight selected points in the scatterplot