Rob J. Hyndman's Blog, page 5

Seasonal functional autoregressive models

Functional autoregressive models are popular for functional time series analysis, but the standard formulation fails to address seasonal behaviour in functional time series data. To overcome this shortcoming, we introduce seasonal functional autoregressive time series models. For the model of order one, we derive sufficient stationarity conditions and limiting behaviour, and provide estimation and prediction methods. Moreover, we consider a portmanteau test for testing the adequacy of this model, and we derive its asymptotic distribution.

Probabilistic forecasts using expert judgment: the road to recovery from COVID-19

The COVID-19 pandemic has had a devastating effect on many industries around the world including tourism and policy makers are interested in mapping out what the recovery path will look like. We propose a novel statistical methodology for generating scenario-based probabilistic forecasts based on a large survey of 443 tourism experts and stakeholders. The scenarios map out pessimistic, most-likely and optimistic paths to recovery. Taking advantage of the natural aggregation structure of tourism data due to geographic locations and purposes of travel, we propose combining forecast reconciliation and forecast combinations implemented to historical data to generate robust COVID-free counterfactual forecasts, to contrast against.

Feature-based time series analysis

Presentation given to Statistical Society of Canada Data Science section It is becoming increasingly common for organizations to collect very large amounts of data over time. Data visualization is essential for exploring and understanding structures and patterns, and to identify unusual observations. However, the sheer quantity of data available means that new time series visualisation methods are needed. I will demonstrate an approach to this problem using a vector of features on each time series, measuring characteristics of the series.

Forecasting the future and the future of forecasting

Blakers Lecture, 17 January 2022 ANU-AAMT National Mathematics Summer School

Job advertisements

Employers often contact me asking how to find a good statistician, econometrician or forecaster for their organization. Students also ask me how to go about finding a job when they finish their degree. This post is for both groups, hopefully making it easier for them to pair up appropriately.General online job sites such as seek or careerjet are ok, but job-seekers can find it hard to find the relevant openings because job titles are so varied.

Job advertisements

Employers often contact me asking how to find a good statistician, econometrician or forecaster for their organization. Students also ask me how to go about finding a job when they finish their degree. This post is for both groups, hopefully making it easier for them to pair up appropriately.

General online job sites such as seek or careerjet are ok, but job-seekers can find it hard to find the relevant openings because job titles are so varied. In the general area of statistics, a job can appear under the titles ���statistician���, ���analyst���, ���data miner���, ���data manager���, ���financial engineer��� and a few dozen other labels. Many employers don���t place the job in the best category, often because they don���t understand what skills are required to do the job. Nevertheless, if I was looking for a job, I would certainly set up some automated searches on these sites.

In statistics, there are well-established job websites that are the best places for both employers and potential employees to meet up.

Australia & New Zealand: careers.statsoc.org.au. This is a fantastic service from the Statistical Society of Australia and includes a lot of jobs, especially those requiring higher degrees.Melbourne Data Science Meetup has a job board with local data science jobs.United States: ASA JobWeb. This is a similar service from the American Statistical Association for jobs in the USA.United Kingdom: RSS Jobs Board. The Royal Statistical Society offers a similar job board for the UK.Math jobs covers jobs in the mathematical sciences.

I do not know what is provided in other countries, but check with your national statistical association.

There are also e-mail lists and web forums that are widely subscribed and often contain job postings.

Australia statistics jobs forum: SSA ForumAustralia and New Zealand econometrics: ANZEcmetUnited Kingdom statistics: AllstatUnited Kingdom econometrics: Econometric-research (for research jobs)Machine Learning News includes posts about machine learning jobs.

If I���ve missed any good places to advertise jobs, please add them in the comments.

Reconstructing missing and anomalous data collected from high-frequency in-situ sensors in fresh waters

In-situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day and week-long sequences of data from a two-year time series of nitrate-concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation and dissolved oxygen data were available.

Feasts & fables: modern tools for time series analysis

EA Cornish Lecture given to the Statistical Society of Australia (South Australian branch): 17 November 2021 Shorter version given at Why R? 2021 Conference: 10 Dec 2021 It is now common for organizations to collect huge amounts of data over time, and existing time series analysis tools are not always able to handle the scale, frequency and structure of the data collected. I will demonstrate some new tools and methods that have been developed to handle the analysis of large collections of time series.

Detecting distributional differences between temporal granularities for exploratory time series analysis

Cyclic temporal granularities are temporal deconstructions of a time period into units such as hour-of-the-day and work-day/weekend. They can be useful for measuring repetitive patterns in large univariate time series data, and feed new approaches to exploring time series data. One use is to take pairs of granularities, and make plots of response values across the categories induced by the temporal deconstruction. However, when there are many granularities that can be constructed for a time period, there will also be too many possible displays to decide which might be the more interesting to display.

LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts

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 popularity of statistical forecasting models such as ETS and ARIMA is their relative simplicity and interpretability (in terms of relevant lags, trend, seasonality, and others), while GFMs typically lack interpretability, especially towards particular time series. This reduces the trust and confidence of the stakeholders when making decisions based on the forecasts without being able to understand the predictions.