Zobrazeno 1 - 10
of 635
pro vyhledávání: '"Hyndman, Rob"'
Autor:
Wang, Xiaoqian, Hyndman, Rob J
We consider the problem of constructing distribution-free prediction intervals for multi-step time series forecasting, with a focus on the temporal dependencies inherent in multi-step forecast errors. We establish that the optimal $h$-step-ahead fore
Externí odkaz:
http://arxiv.org/abs/2410.13115
Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existing methods for detecting abnormal graphs can suffer from multiple limi
Externí odkaz:
http://arxiv.org/abs/2410.05687
A novel forecast linear augmented projection (FLAP) method is introduced, which reduces the forecast error variance of any unbiased multivariate forecast without introducing bias. The method first constructs new component series which are linear comb
Externí odkaz:
http://arxiv.org/abs/2407.01868
Autor:
Gamakumara, Puwasala, Santos-Fernandez, Edgar, Talagala, Priyanga Dilini, Hyndman, Rob J., Mengersen, Kerrie, Leigh, Catherine
Time series often reflect variation associated with other related variables. Controlling for the effect of these variables is useful when modeling or analysing the time series. We introduce a novel approach to normalize time series data conditional o
Externí odkaz:
http://arxiv.org/abs/2305.12651
Forecast reconciliation is a post-forecasting process that involves transforming a set of incoherent forecasts into coherent forecasts which satisfy a given set of linear constraints for a multivariate time series. In this paper we extend the current
Externí odkaz:
http://arxiv.org/abs/2303.17277
Detecting anomalies from a series of temporal networks has many applications, including road accidents in transport networks and suspicious events in social networks. While there are many methods for network anomaly detection, statistical methods are
Externí odkaz:
http://arxiv.org/abs/2210.07407
Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities. Combining multiple forecasts produced from single (target) series is now wi
Externí odkaz:
http://arxiv.org/abs/2205.04216
Global Forecasting Models (GFM) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compared with univariate forecasting approaches. One aspect of the populari
Externí odkaz:
http://arxiv.org/abs/2111.07001
Publikováno v:
In International Journal of Forecasting July-September 2024 40(3):1134-1151
Forecast evaluation plays a key role in how empirical evidence shapes the development of the discipline. Domain experts are interested in error measures relevant for their decision making needs. Such measures may produce unreliable results. Although
Externí odkaz:
http://arxiv.org/abs/2108.03588