Professor Rob J Hyndman

Rob J Hyndman

Functionalization of microarray devices: process optimization using a multiobjective PSO and multiresponse MARS modeling

2010 Feb

In Working papers

L. Villanova, P. Falcaro, D. Carta, I. Poli, R. J. Hyndman, K. Smith-Miles

Abstract: An evolutionary approach for the optimization of microarray coatings produced via sol-gel chemistry is presented. The aim of the methodology is to face the challenging aspects of the problem: high dimensional variable space, constraints on the independent variables, multiple responses, expensive or time-consuming experimental trials, expected complexity of the functional relationships between independent and response variables. The proposed approach iteratively select a set of experiments by combining a multiobjective Particle Swarm Optimization (PSO) and a multiresponse Multivariate Adaptive Regression Spines (MARS) model. At each iteration of the algorithm the selected experiments are implemented and evaluated, and the system response is used as a feedback for the selection of the new trials. The best coating identified using the described methodology is characterized by relevant improvements with respect to the best coating obtained changing one variable at a time. The proposed evolutionary approach is shown to be a useful methodology for process optimization with great promise for industrial applications.

How Jewish mythology helps us understand the New Testament

2010 Jan

In Bible talks

The New Testament contains many allusions, and some direct quotations, of Jewish myths that circulated in the first century. In this talk (given at the Dandenong Bible Education Centre), I explore the most important examples thereby providing explanations of several otherwise puzzling passages. The talk covers demons, Beelzebul, Belial, Satan as an “angel of light”, the “angels that sinned”, and the parable of the rich man and Lazarus. Read the rest of this entry »

Detecting trend and seasonal changes in satellite image time series

2010 Jan

In Refereed papers

Jan Verbesselt1, Rob J Hyndman2, Glenn Newnham1, Darius Culvenor1
Remote Sensing of Environment (2010), 114(1), 106-115.
  1. Remote sensing team, CSIRO Sustainable Ecosystems, Private Bag 10, Melbourne VIC 3169, Australia
  2. Department of Econometrics and Business Statistics, Monash University, Melbourne VIC 3800, Australia
Abstract

A wealth of remotely sensed time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the detection of different types of changes. Changes occurring in the trend component indicate disturbances (e.g., insect attack), while changes occurring in the seasonal component indicate phenological changes (e.g., change in land cover type). An approach is proposed for automated change detection in time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting significant change within time series. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested BFAST by simulating 16-day composites of Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that BFAST can robustly detect change with different magnitudes (>0.1 NDVI) within time series with different noise levels (0.01–0.07 σ) and seasonal amplitudes (0.1–0.5 NDVI) Additionally, BFAST was applied to 16-day NDVI MODIS (Moderate Resolution Imaging Spectroradiometer) composites for a forested study area in south eastern Australia. This showed that BFAST is able to detect and characterize spatial and temporal changes in a forested landscape. BFAST is developed as a generic change detection approach, and can be applied to time series without the need to normalize for specific land cover types, select a reference period, or define a threshold or change trajectory. The method can be used to detect and characterize changes within time series or can be integrated within monitoring frameworks and used as an alarm system to flag when and where significant changes occur.

Online paper

Density forecasting for long-term peak electricity demand

2010 Jan

In Refereed papers

Rob J Hyndman and Shu Fan
IEEE Transactions on Power Systems, 2010, to appear.

Abstract: Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long term context, planners must adopt a probabilistic view of potential peak demand levels, therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. This paper proposes a new methodology to forecast the density of long-term peak electricity demand.

Peak electricity demand in a given season is subject to a range of uncertainties, including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays.

We describe a comprehensive forecasting solution in this paper. First, we use semi-parametric additive models to estimate the relationships between demand and the driver variables, including temperatures, calendar effects and some demographic and economic variables. Then we forecast the demand distributions using a mixture of temperature simulation, assumed future economic scenarios, and residual bootstrapping. The temperature simulation is implemented through a new seasonal bootstrapping method with variable blocks.

The proposed methodology has been used to forecast the probability distribution of annual and weekly peak electricity demand for South Australia since 2007. We evaluate the performance of the methodology by comparing the forecast results with the actual demand of the summer 2007/08.

Keywords: Long-term demand forecasting, density forecast, time series, simulation.

Online article

Rainbow plots, bagplots and boxplots for functional data

2010 Jan

In Refereed papers

Rob J Hyndman and Han Lin Shang
Journal of Computational and Graphical Statistics (2010), to appear

Abstract: We propose new tools for visualizing large numbers of functional data in the form of smooth curves or surfaces. The proposed tools include functional versions of the bagplot and boxplot, and make use of the first two robust principal component scores, Tukey’s data depth and highest density regions.

By-products of our graphical displays are outlier detection methods for functional data. We compare these new outlier detection methods with existing methods for detecting outliers in functional data and show that our methods are better able to identify the outliers.

Keywords: Highest density regions, Robust principal component analysis, Kernel density estimation, Outlier detection, Tukey’s halfspace depth.

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R package

Using functional data analysis models to estimate future time trends of age-specific breast cancer mortality for the United States and England-Wales

2010 Jan

In Refereed papers

Bircan Erbas1, Muhammad Akram2, Dorota M Gertig3, Dallas English4,5, John L. Hopper5, Anne M Kavanagh6 and Rob J Hyndman2
Journal of Epidemiology (2010), to appear
  1. School of Public Health, La Trobe University, Bundoora, 3086 Australia
  2. Business and Economic Forecasting Unit, Monash University, Clayton, 3800, Australia.
  3. Victoria Cytology Service Inc, Carlton, 3053 Australia.
  4. Cancer Epidemiology Centre, The Cancer Council Victoria, Carlton 3053 Australia.
  5. Centre for MEGA Epidemiology, The University of Melbourne, Parkville 3053 Australia.
  6. Key Centre for Women’s Health in Society, School of Population Health, The University of Melbourne, Parkville, 3053 Australia.
ABSTRACT

Background: Mortality/incidence predictions are used for planning public health resources and need to accurately reflect age-related changes through time. We present a new forecasting model to estimate future trends in age-related breast cancer mortality for the United States and England-Wales.

Material and methods: We use functional data analysis techniques to model breast cancer mortality-age relationships in the United States from 1950 to 2001 and England-Wales from 1950 to 2003, and estimate 20-year predictions using a new
forecasting method.

Results: In the United States, trends for women aged 45–54 years continued to decline since 1980. In contrast, trends in women aged 60 – 84 years increased in the 1980s and declined in the 1990s. For England-Wales, trends for women aged 45 to 74 years slightly increased prior to 1980, but declined thereafter. The greatest age-related changes for both countries were during the 1990s. For both the United States and England-Wales, trends are expected to decline and then stabilize with the greatest decline in women aged 60 – 70 years. Forecasts suggest relatively stable trends for women over 75 years.

Conclusions: Predicting age related changes in mortality/incidence can be used for planning and targeting programs for specific age groups. Currently, these models are being extended to incorporate other variables that may influence age-related changes in mortality/incidence trends. In their current form, these models will be most useful for modelling and projecting future trends of diseases where there has been very little advancement in treatment and minimal cohort effects such as lethal cancers.

Key words: breast cancer, forecasting, functional-data-analysis models, mortality trends

Online paper

Encouraging replication and reproducible research

2010 Jan

In Editorials

Rob J Hyndman
International Journal of Forecasting (2010), 26(1), pp.2-3.

Online editorial

Changing of the guard

2010 Jan

In Editorials

Rob J Hyndman
International Journal of Forecasting (2010), 26(1), p1.

Online editorial

The vector innovations structural time series framework: a simple approach to multivariate forecasting

2010 Jan

In Refereed papers

Ashton de Silva1, Rob J Hyndman2 and Ralph D Snyder2
Statistical modelling (2010), to appear.
  1. School of Economics, Finance and Marketing, RMIT, VIC 3000, Australia.
  2. Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia.

Abstract The vector innovations structural time series framework is proposed as a way of modelling a set of related time series. Like all multivariate approaches, the aim is to exploit potential inter-series dependencies to improve the fit and forecasts. The model is based around an unobserved vector of components representing features such as the level and slope of each time series. Equations that describe the evolution of these components through time are used to represent the inter-temporal dependencies. The approach is illustrated on a bivariate data set comprising Australian exchange rates of the UK pound and US dollar. The forecasting accuracy of the new modelling framework is compared to other common uni- and multivariate approaches in an experiment using time series from a large macroeconomic database.

Keywords: vector innovations structural time series, state space model, multivariate time series, exponential smoothing, forecast comparison, vector autoregression.

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Forecasting time series with complex seasonal patterns using exponential smoothing

2009 Dec

In Working papers

Alysha M De Livera and Rob J Hyndman

Abstract
A new innovations state space modeling framework, incorporating Box-Cox transformations, Fourier series with time varying coefficients and ARMA error correction, is introduced for forecasting complex seasonal time series that cannot be handled using existing forecasting models. Such complex time series include time series with multiple seasonal periods, high frequency seasonality, non-integer seasonality and dual-calendar effects. Our new modelling framework provides an alternative to existing exponential smoothing models, and is shown to have many advantages. The methods for initialization and estimation, including likelihood evaluation, are presented, and analytical expressions for point forecasts and interval predictions under the assumption of Gaussian errors are derived, leading to a simple, comprehensible approach to forecasting complex seasonal time series. Our trigonometric formulation is also presented as a means of decomposing complex seasonal time series, which cannot be decomposed using any of the existing decomposition methods. The approach is useful in a broad range of applications, and we illustrate its versatility in three empirical studies where it demonstrates excellent forecasting performance over a range of prediction horizons. In addition, we show that our trigonometric decomposition leads to the identification and extraction of seasonal components, which are otherwise not apparent in the time series plot itself.

Keywords: exponential smoothing, Fourier series, prediction intervals, seasonality, state space models, time series decomposition.

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