In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

System Identification I

In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values.

Position paper: Characterising performance of environmental models

Microbial fuel cells: From fundamentals to applications. A review

Machine learning methods for solar radiation forecasting: A review

Is microbial fuel cell technology ready? An economic answer towards industrial commercialization

https://kourentzes.com/forecasting/wp-content/uploads/2014/04/summary_MAPA.pdf

Improving forecasting by estimating time series structural components across multiple frequencies

Recursive Estimation and Time-Series Analysis. An Introduction for the Student and Practitioner, Second edition, Peter C. Young. Springer (2011), 504 pp., Hardcover, $119.00, ISBN: 978-3-642-21980-1

Forecasts of demand are crucial to drive supply chains and enterprise resource planning systems. Usually, well-known univariate methods that work automatically such as exponential smoothing are employed to accomplish such forecasts. The traditional Supply Chain relies on a decentralized system where each member feeds its own Forecasting Support System (FSS) with incoming orders from direct customers. Nevertheless, other collaboration schemes are also possible, for instance, the Information Exchange framework allows demand information to be shared between the supplier and the retailer. Current theoretical models have shown the limited circumstances where retailer information is valuable to the supplier. However, there has been very little empirical work carried out. Considering a serially linked two-level supply chain, this work assesses the role of sharing market sales information obtained by the retailer on the supplier forecasting accuracy. Weekly data from a manufacturer and a major UK grocery retailer have been analyzed to show the circumstances where information sharing leads to improved forecasting accuracy. Without resorting to unrealistic assumptions, we find significant evidence of benefits through information sharing with substantial improvements in forecast accuracy.

/pdf/impact-of-information-exchange-on-supplier-forecasting-3mjriiquic.pdf

Juan R. Trapero

Papers

Is microbial fuel cell technology ready? An economic answer towards industrial commercialization

Improving forecasting by estimating time series structural components across multiple frequencies

Recursive Estimation and Time-Series Analysis. An Introduction for the Student and Practitioner, Second edition, Peter C. Young. Springer (2011), 504 pp., Hardcover, $119.00, ISBN: 978-3-642-21980-1

Impact of Information Exchange on Supplier Forecasting Performance

An algebraic frequency estimator for a biased and noisy sinusoidal signal