Visualising forecasting algorithm performance using time series instance spaces
Year of publication: |
April-June 2017
|
---|---|
Authors: | Kang, Yanfei ; Hyndman, Rob J. ; Smith-Miles, Kate |
Published in: |
International journal of forecasting. - Amsterdam [u.a.] : Elsevier, ISSN 0169-2070, ZDB-ID 283943-X. - Vol. 33.2017, 2, p. 345-358
|
Subject: | M3-Competition | Time series visualisation | Time series generation | Forecasting algorithm comparison | Zeitreihenanalyse | Time series analysis | Prognoseverfahren | Forecasting model | Theorie | Theory | Algorithmus | Algorithm |
-
Visualising forecasting algorithm performance using time series instance spaces
Kang, Yanfei, (2016)
-
Demand forecasting with four-parameter exponential smoothing
Ferbar Tratar, Liljana, (2016)
-
Models for optimising the theta method and their relationship to state space models
Fiorucci, Jose A., (2016)
- More ...
-
Visualising forecasting algorithm performance using time series instance spaces
Kang, Yanfei, (2016)
-
Efficient generation of time series with diverse and controllable characteristics
Kang, Yanfei, (2018)
-
Distributed ARIMA models for ultra-long time series
Wang, Xiaoqian, (2020)
- More ...