T
Thiyanga S. Talagala
Researcher at University of Sri Jayewardenepura
Publications - 17
Citations - 556
Thiyanga S. Talagala is an academic researcher from University of Sri Jayewardenepura. The author has contributed to research in topics: Time series & Interpretability. The author has an hindex of 6, co-authored 14 publications receiving 271 citations. Previous affiliations of Thiyanga S. Talagala include Monash University.
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FFORMA: Feature-based forecast model averaging
TL;DR: This work uses a collection of time series to train a meta-model for assigning weights to various possible forecasting methods with the goal of minimizing the average forecasting loss obtained from a weighted forecast combination.
Repository
Forecasting: theory and practice
Fotios Petropoulos,Daniele Apiletti,Vassilios Assimakopoulos,Mohamed Zied Babai,Devon K. Barrow,Souhaib Ben Taieb,Christoph Bergmeir,Ricardo J. Bessa,Jakub Bijak,John E. Boylan,Jethro Browell,Claudio Carnevale,Jennifer L. Castle,Pasquale Cirillo,Michael P. Clements,Clara Cordeiro,Clara Cordeiro,Fernando Luiz Cyrino Oliveira,Shari De Baets,Alexander Dokumentov,Joanne Ellison,Piotr Fiszeder,Philip Hans Franses,David T. Frazier,Michael Gilliland,M. Sinan Gönül,Paul Goodwin,Luigi Grossi,Yael Grushka-Cockayne,Mariangela Guidolin,Massimo Guidolin,Ulrich Gunter,Xiaojia Guo,Renato Guseo,Nigel Harvey,David F. Hendry,Ross Hollyman,Tim Januschowski,Jooyoung Jeon,Victor Richmond R. Jose,Yanfei Kang,Anne B. Koehler,Stephan Kolassa,Nikolaos Kourentzes,Nikolaos Kourentzes,Sonia Leva,Feng Li,Konstantia Litsiou,Spyros Makridakis,Gael M. Martin,Andrew B. Martinez,Andrew B. Martinez,Sheik Meeran,Theodore Modis,Konstantinos Nikolopoulos,Dilek Önkal,Alessia Paccagnini,Alessia Paccagnini,Anastasios Panagiotelis,Ioannis P. Panapakidis,Jose M. Pavía,Manuela Pedio,Manuela Pedio,Diego J. Pedregal,Pierre Pinson,Patrícia Ramos,David E. Rapach,J. James Reade,Bahman Rostami-Tabar,Michał Rubaszek,Georgios Sermpinis,Han Lin Shang,Evangelos Spiliotis,Aris A. Syntetos,Priyanga Dilini Talagala,Thiyanga S. Talagala,Len Tashman,Dimitrios D. Thomakos,Thordis L. Thorarinsdottir,Ezio Todini,Juan Ramón Trapero Arenas,Xiaoqian Wang,Robert L. Winkler,Alisa Yusupova,Florian Ziel +84 more
TL;DR: A non-systematic review of the theory and the practice of forecasting, offering a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts.
Posted Content
Meta-learning how to forecast time series
TL;DR: A random forest is used to identify the best forecasting method using only time series features and is shown to yield accurate forecasts comparable to several benchmarks and other commonly used automated approaches of time series forecasting.
Journal ArticleDOI
Meta‐learning how to forecast time series
TL;DR: A random forest is used to identify the best forecasting method using only time series features and is shown to yield accurate forecasts comparable to several benchmarks and other commonly used automated approaches of time series forecasting.
Posted Content
FFORMPP: Feature-based forecast model performance prediction
TL;DR: A novel meta-learning algorithm for time series forecasting using the efficient Bayesian multivariate surface regression approach to model forecast error as a function of features calculated from the time series.