activity
20182026
most citedMachine learning applications in time series hierarchical forecasting

14 citations · 37 across the 18 of their papers we have counts for

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7 papers · 1 filter

stat.ME2026

When lookout sees crackle: Anomaly detection via kernel density estimation

Rob J Hyndman, Sevvandi Kandanaarachchi, Katharine Turner

We present an updated version of lookout -- an algorithm for detecting anomalies using kernel density estimates with bandwidth based on Rips death diameters -- with theoretical gua…

stat.ME2026

Anomaly detection using surprisals

Rob J Hyndman, David T. Frazier

Anomaly detection methods are widely used but often rely on ad hoc rules or strong assumptions, and they often focus on tail events, missing ``inlier'' anomalies that occur in low-…

stat.ME20241 cited

Online conformal inference for multi-step time series forecasting

Xiaoqian Wang, Rob J Hyndman

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-s…

stat.ME2024

Forecast Linear Augmented Projection (FLAP): A free lunch to reduce forecast error variance

Yangzhuoran Fin Yang, George Athanasopoulos, Rob J. Hyndman +1

A novel forecast linear augmented projection (FLAP) method is introduced, which reduces the forecast error variance of any unbiased multivariate forecast without introducing bias.…

stat.ME2023

Conditional normalization in time series analysis

Puwasala Gamakumara, Edgar Santos-Fernandez, Priyanga Dilini Talagala +3

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…

stat.ME2023

Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues

Daniele Girolimetto, George Athanasopoulos, Tommaso Di Fonzo +1

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 constrain…