99 citations · 312 across the 30 of their papers we have counts for
34 papers
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…
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-…
ggtime: A Grammar of Temporal Graphics
Cynthia A. Huang, Mitchell O'Hara-Wild, Rob J. Hyndman +1
Visualizing changes over time is fundamental to learning from the past and anticipating the future. However, temporal semantics can be complicated, and existing visualization tools…
Good intentions, unintended consequences: exploring forecasting harms
Bahman Rostami-Tabar, Travis Greene, Galit Shmueli +1
Organizations worldwide that rely on data-driven approaches regularly employ forecasting methods to enhance their planning and decision-making processes. While extensive research h…
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…
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs
Sevvandi Kandanaarachchi, Conrad Sanderson, Rob J. Hyndman
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. Existi…
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.…
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…
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…
Anomaly detection in dynamic networks
Sevvandi Kandanaarachchi, Rob J Hyndman
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 a…
Forecast combinations: an over 50-year review
Xiaoqian Wang, Rob J Hyndman, Feng Li +1
Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities. Combi…
LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts
Dilini Rajapaksha, Christoph Bergmeir, Rob J Hyndman
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 compa…
A Look at the Evaluation Setup of the M5 Forecasting Competition
Hansika Hewamalage, Pablo Montero-Manso, Christoph Bergmeir +1
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…
MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns
Kasun Bandara, Rob J Hyndman, Christoph Bergmeir
The decomposition of time series into components is an important task that helps to understand time series and can enable better forecasting. Nowadays, with high sampling rates lea…
Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis
Insha Ullah, Kerrie Mengersen, Rob J Hyndman +1
Organizations such as government departments and financial institutions provide online service facilities accessible via an increasing number of internet connected devices which ma…
Understanding links between water-quality variables and nitrate concentration in freshwater streams using high-frequency sensor data
Claire Kermorvant, Benoit Liquet, Guy Litt +5
Real time monitoring using in situ sensors is becoming a common approach for measuring water quality within watersheds. High frequency measurements produce big data sets that prese…
Monash Time Series Forecasting Archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb +2
Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are t…
Computationally Efficient Learning of Statistical Manifolds
Fan Cheng, Anastasios Panagiotelis, Rob J Hyndman
Analyzing high-dimensional data with manifold learning algorithms often requires searching for the nearest neighbors of all observations. This presents a computational bottleneck i…
Modern strategies for time series regression
Stephanie Clark, Rob J Hyndman, Dan Pagendam +1
This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classica…
Model selection in reconciling hierarchical time series
Mahdi Abolghasemi, Rob J Hyndman, Evangelos Spiliotis +1
Model selection has been proven an effective strategy for improving accuracy in time series forecasting applications. However, when dealing with hierarchical time series, apart fro…
Visualizing probability distributions across bivariate cyclic temporal granularities
Sayani Gupta, Rob J Hyndman, Dianne Cook +1
Deconstructing a time index into time granularities can assist in exploration and automated analysis of large temporal data sets. This paper describes classes of time deconstructio…
STR: Seasonal-Trend Decomposition Using Regression
Alexander Dokumentov, Rob J. Hyndman
We propose a new method for decomposing seasonal data: STR (a Seasonal-Trend decomposition using Regression). Unlike other decomposition methods, STR allows for multiple seasonal a…
Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality
Pablo Montero-Manso, Rob J Hyndman
Forecasting groups of time series is of increasing practical importance, e.g. forecasting the demand for multiple products offered by a retailer or server loads within a data cente…
Distributed ARIMA Models for Ultra-long Time Series
Xiaoqian Wang, Yanfei Kang, Rob J Hyndman +1
Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This…