Fotios Petropoulos

Fotios Petropoulos
University of Bath | UB · Faculty of Management

26.6
 · 
Diploma (MEng), DEng
About
58
Research items
9,974
Reads
453
Citations
Introduction
Fotios Petropoulos is Associate Professor at the School of Management of the University of Bath, elected Director of the International Institute of Forecasters, Associate Editor at the International Journal of Forecasting and the Forecasting Support Systems Editor of Foresight. His research expertise lies in behavioural aspects of forecasting and improving the forecasting process, applied in the context of business and supply chain.
Research Experience
Sep 2014
Cardiff University
Position
  • Lecturer (Assistant Professor)
Sep 2012 - Aug 2014
Lancaster University
Position
  • Senior Research Associate
Oct 2003 - Aug 2012
National Technical University of Athens
Position
  • Unit Co-cordinator, Research Associate, Research Assistant
Network
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Following
Projects
Projects (2)
Project
We propose to produce probabilistic forecasts for a group of households rather than individual households that minimise forecast uncertainty. Our study indicates that probabilistic forecast accuracy enhances with a larger aggregation of customers, and converges to an average maximum for groups larger than 50 customers, the number of minimum customers required in P2P when the aggregation is constructed in a random manner. The research further suggests methods for defining a group of customers, which allows to have a better probabilistic forecast accuracy, for an expected aggregated demand given. Among the three different methods we explored, the simplest method using standard deviation in the in-sample period performs as good as the others. Our empirical analysis uses hourly data from Korea and Ireland to evaluate density forecasts, up to 24 h ahead. This approach is useful for peer to peer (P2P) energy trading that aims to utilise surplus in renewable energy generation locally. Our research indicate that a simple method based on the standard deviation calculation using in-sample data performs as good as more complicated and time consuming approaches.
Project
Industry globalization is making that Spanish and European companies should become more competitive. In that sense, it is crucial to improve the supply chain management (SCM) in order to reduce costs and enhance customer service level. In fact, many authors claim that the strategy should be focused on obtaining a global supply chain optimum rather than local suboptimum. Nonetheless, traditionally, supply chain members establish a minimal collaboration between them. This type of SCM is known to be responsible of an undesired phenomenon called the bullwhip effect, which consists of the demand variability amplification when moving upwards in the supply chain. This effect is behind of losing up to 30% of the supply chain profits. Among the consequences of this amplification, for instance, we might find excess inventory, poor customer service and poor product forecasts. In order to reduce the bullwhip effect different collaborative schemes in terms of planning and/or replenishment policies have been proposed. Unfortunately, the industry application of these collaborative schemes have turned out not to be as successful as it was expected. In particular, different case studies describe how, despite the considerable investment in a business software, which enables the information exchange between echelons in the supply chain, the returns obtained were not satisfactory. Essentially, although the companies had available an increasing vast amount of information of other supply chain echelons or even external information coming from social networks (big data), their business intelligence schemes particularized in traditional forecasting and replenishment policies could not handle efficiently such new valuable information. Therefore, the aim of this project is to investigate novel forecasting and stock control methodologies capable of incorporating efficiently the key information from the rest of the supply chain and its environment to achieve a considerable reduction of costs as a consequence of the bullwhip effect reduction. Simulation results will provide the theoretical basis to determine the improvement extent obtained by the development of the proposed forecasting and stock control techniques under collaboration between companies’ schemes. In order to validate the results of the project, a dataset comprising sales, shipments, promotional activities and forecasts obtained from companies interested in our research will be also tested
Research
Research items (58)
Article
Full-text available
In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model-build approach, in which experts were asked to decide on the existence of the s...
Article
In a recent study, Bergmeir, Hyndman and Benítez (2016) successfully employed a bootstrap aggregation (bagging) technique for improving the performance of exponential smoothing. Each series is Box-Cox transformed, and decomposed by Seasonal and Trend decomposition using Loess (STL); then bootstrapping is applied on the remainder series before the t...
Article
Full-text available
This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust foreca...
Article
Full-text available
Forecasting competitions have been a major drive not only for improving the performance of forecasting methods but also for the development of new forecasting approaches. Despite the tremendous value and impact of these competitions, they suffer from the limitation is that performance is measured only in terms of forecast accuracy and bias, lacking...
Preprint
Interval forecasts have significant advantages in providing uncertainty estimation to point forecasts, leading to the importance of providing prediction intervals (PIs) as well as point forecasts. In this paper, we propose a general feature-based time series forecasting framework, which is divided into "offline" and "online" parts. In the "offline"...
Article
Full-text available
New methods are proposed for adjusting probabilistic forecasts to ensure coherence with the ag-gregation constraints inherent in temporal hierarchies. The different approaches nested within this framework include methods that exploit information at all levels of the hierarchy as well as a novel method based on cross-validation. The methods are eval...
Chapter
This chapter reports some results in the literature as well as some new results that show the potential of extending the standard Theta method. It describes the data used to produce the results as well as how performance is measured. Forecasting performance is measured by means of the symmetric mean absolute percentage error (sMAPE). The standard t...
Article
Full-text available
This paper describes the approach that we implemented to produce the point forecasts and prediction intervals for the M4-competition submission. The proposed Simple Combination of Univariate Models (SCUM) is the median combination of the point forecasts and prediction intervals of four models, namely Exponential Smoothing, Complex Exponential Smoot...
Article
Full-text available
In this article, we shed light on the differences between two judgmental forecasting approaches for model selection – forecast selection and pattern identification – with regard to their forecasting performance and underlying cognitive processes. We designed a laboratory experiment using real-life time series as stimuli to record subjects’ selectio...
Preprint
Full-text available
New methods are proposed for adjusting probabilistic forecasts to ensure coherence with the aggregation constraints inherent in temporal hierarchies. The different approaches nested within this framework include methods that exploit information at all levels of the hierarchy as well as a novel method based on cross-validation. The methods are evalu...
Article
Full-text available
Forecast selection and combination are regarded as two competing alternatives. In the literature there is substantial evidence that forecast combination is beneficial, in terms of reducing the forecast errors, as well as mitigating modelling uncertainty as we are not forced to choose a single model. However , whether all forecasts to be combined ar...
Article
We propose the use of hierarchical structures for forecasting freight earnings. Hierarchical time series approaches are applied in the dry-bulk and tanker markets to identify the most suitable strategy for forecasting freight rates. We argue that decision making for shipping practitioners should be based on forecasts of freight earnings at differen...
Article
Full-text available
Simple moving average (SMA) is a well-known forecasting method. It is easy to understand and interpret and easy to use, but it does not have an appropriate length selection mechanism and does not have an underlying statistical model. In this paper, we show two statistical models underlying SMA and demonstrate that the automatic selection of the opt...
Article
Traditionally, forecasters focus on the development algorithms to identify optimal models and sets of parameters, optimal in the sense of within-sample fitting. However, this quest strongly assumes that optimally set parameters will also give the best extrapolations. The problem becomes even more pertinent when we consider the vast volumes of data...
Article
In forecast value added analysis, the accuracy of relatively sophisticated forecasting methods is compared to that of naïve 1 forecasts to see whether the extra costs and effort of implementing them are justified. In this note, we derive a ratio that indicates the upper bound of a forecasting method’s accuracy relative to naïve 1 forecasts when the...
Article
Demand forecasting is a crucial input of any inventory system. The quality of the forecasts should be evaluated not only in terms of forecast accuracy or bias but also with regards to their inventory implications, which include the impact on the total inventory cost, the achieved service levels and the variance of orders and inventory. Forecast sel...
Article
Full-text available
In this paper we focus on forecasting for intermittent demand data. We propose a new aggregation framework for intermittent demand forecasting that performs aggregation over the demand volumes, in contrast to the standard framework that employs temporal (over time) aggregation. To achieve this we construct a transformed time series, the inverse int...
Article
Full-text available
Accurate and robust forecasting methods for univariate time series are very important when the objective is to produce estimates for a large number of time series. In this context, the Theta method caught researchers' attention due to its performance in the M3-Competition. The Theta method, as implemented in the monthly subset of the M3-Competition...
Article
Full-text available
Several biases and inefficiencies are commonly associated with the judgmental extrapolation of time series even when forecasters have technical knowledge about forecasting. This study examines the effectiveness of using a rolling training approach, based on feedback, to improve the accuracy of forecasts elicited from people with such knowledge. In...
Research
Full-text available
Demand forecasting is a crucial input of any inventory system. The quality of the forecasts should be evaluated not only in terms of forecast accuracy or bias, but also with regards to their inventory implications, which include the impact on the inventory cost, the achieved service levels and the variance of orders and inventory. Forecast selectio...
Article
Full-text available
Demand forecasting is central to decision making and operations in organisations. As the volume of forecasts increases, for example due to an increased product customisation that leads to more SKUs being traded, or a reduction in the length of the forecasting cycle, there is a pressing need for reliable automated forecasting. Conventionally, compan...
Conference Paper
Forecasting Support Systems (FSSs) refer to a set of processes with an objective of accurately forecasting variables of interest within a company. Such systems may not be limited to a set of statistical methods, but also include interactions between statistical outputs and management judgment, procedures for storing, retrieving and presenting infor...
Article
Full-text available
The behaviour of poker players and sports gamblers has been shown to change after winning or losing a significant amount of money on a single hand. In this paper, we explore whether there are changes in experts’ behaviour when performing judgmental adjustments to statistical forecasts and, in particular, examine the impact of ‘big losses’. We defin...
Article
Armstrong, Green, and Graefe (this issue) propose the Golden Rule in forecasting: “be conservative”. According to the authors, the successful application of the Golden Rule comes through a checklist of 28 guidelines. Even if the authors of this commentary embrace the main ideas around the Golden Rule, which targets to address the “average” situatio...
Article
Selecting the appropriate forecasting method for a large number of time series is a major problem for many organizational forecaster. Researchers propose various selection rules in order to enhance forecasting accuracy. The simpler approach for model selection involves the identification of a single method, which is applied to all data series in an...
Article
Full-text available
Accurate and robust forecasting methods for univariate time series are very important when the objective is to produce estimates for a large number of time series. In this context, the Theta method called researchers attention due its performance in the largest up-to-date forecasting competition, the M3-Competition. Theta method proposes the decomp...
Article
Nowadays, informed decision making is conducted through innovative Information and Communication Technology (ICT) support systems. In order to utilize such ICT-based support systems fully, decision makers need suitable training. This paper proposes and evaluates the use of a Forecasting and Foresight Support System in an undergraduate course in bus...
Article
Forecasting special events such as conflicts and epidemics is challenging because of their nature and the limited amount of historical information from which a reference base can be built. This study evaluates the performances of structured analogies, the Delphi method and interaction groups in forecasting the impact of such events. The empirical e...
Article
Full-text available
Forecasting as a scientific discipline has progressed a lot in the last 40 years, with Nobel prizes being awarded for seminal work in the field, most notably to Engle, Granger and Kahneman. Despite these advances, even today we are unable to answer a very simple question, the one that is always the first tabled during discussions with practitioners...
Article
Full-text available
In most business forecasting applications, the problem usually directs the sampling frequency of the data that we collect and use for forecasting. Conventional approaches try to extract information from the historical observations to build a forecasting model. In this article, we explore how trans-forming the data through temporal aggregation allow...
Presentation
Full-text available
Forecasting energy demand in building and device level simultaneously is a complex process. Its performance, in terms of accuracy, depends on both the characteristics of the individual devices and the facility as a whole, not to mention weather conditions and timetables. The structure of the problem lends itself for hierarchical forecasting. We exa...
Article
Full-text available
Intermittent demand is characterised by infrequent demand arrivals, where many periods have zero demand, coupled with varied demand sizes. The dual source of variation renders forecasting for intermittent demand a very challenging task. Many researchers have focused on the development of specialised methods for intermittent demand. However, apart f...
Article
Full-text available
Identifying the appropriate time series model to achieve good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing accuracy. From the original time series, using temporal aggregation, multiple time series are constructed. These derivative series highlight...
Article
Full-text available
This paper is an attempt to gain mathematical insight into the aggregate-disaggregate intermittent demand approach (ADIDA) forecasting framework, by formulating it as a multi-rate signal processing system. After a brief synopsis of the framework’s background, an alternative way to perceive ADIDA from a systemic viewpoint is derived by breaking down...
Article
Full-text available
Purpose – Intermittent demand appears sporadically, with some time periods not even displaying any demand at all. Even so, such patterns constitute considerable proportions of the total stock in many industrial settings. Forecasting intermittent demand is a rather difficult task but of critical importance for corresponding cost savings. The current...
Article
Forecasting accuracy and performance of extrapolation techniques has always been of major importance for both researchers and practitioners. Towards this direction, many forecasting competitions have conducted over the years, in order to provide solid performance measurement frameworks for new methods. The Theta model outperformed all other partici...
Article
'Digital strategy' is an EU-level policy promoting the use of ICT in houses and companies across Europe, so as to improve standards of living and competitiveness respectively. This article presents a web information system that forecasts the potential success of such policies at country and regional level. The proposed system provides guidance for...
Article
Governments often use budget so as to provide incentives for citizens to adopt new policies, especially when these are promoting eco-friendly technologies e.g. to subsidise the price of a hybrid-car. The public money spent on each policy, is considered to be value-for–money only if many citizens do adopt the proposed policy. This is also known as t...
Article
Apple is the largest publicly traded company in the world by market capitalisation, and also the largest technology company in the world by revenue and profit. Accurate forecasting unit sales is of major importance as even the smallest of errors will have a huge impact in terms of sales management and revenues. The current research investigates ext...
Article
Full-text available
Beets’ cultivation and sugar production represent one of the most important parts of Greek agricultural economy. A careful and well-organized planning of the production as well as the determination of an accurate safety stock is important for sugar industry, as for many other companies and organizations, in order to define the production quantity w...
Article
Full-text available
Intermittent demand patterns are characterised by infrequent demand arrivals coupled with variable demand sizes. Such patterns prevail in many industrial applications, including IT, automotive, aerospace and military. An intuitively appealing strategy to deal with such patterns from a forecasting perspective is to aggregate demand in lower-frequenc...
Article
Full-text available
This research introduces a specialised information support system that was designed and implemented for project management and financial monitoring in public sector of funded projects, under the Greek National Strategic Reference Framework (NSRF). Its goal was to ensure effective data monitoring and controlling for the Ministry of National Defense....
Article
This article empirically investigates the extension of the use of an aggregation-disaggregation forecasting approach for intermittent demand (ADIDA) to fast-moving demand data, addressing the need of supply chain managers for accurate forecasts. After a brief introduction to the framework and its background, an experiment is set up to examine its p...
Article
Full-text available
The Theta model created a lot of interest in academic circles due to its surprising performance in the M3-competition, the biggest ever time series forecasting competition. As a result in the subsequent years it became a benchmark in any empirical forecasting exercise and an essential tool for efficient Supply Chain Management ad planning as it pro...
Article
Nowadays financial markets are facing continuous values’ fluctuations, resulting in higher risks that eventually influence investors’ decisions. In this article a methodology is proposed in order to efficiently build portfolios of futures. The new methodology is tested on data from the derivative indices FTSE/ASE-20 and FTSE/ASA MID 40 in Greece. T...
Article
The Theta model created a lot of interest in academic circles due to its surprisingly good performance in the M3 forecasting competition. However, this interest was not followed up by other studies, with the exception of Hyndman and Billah in 2003. In addition, the Theta model performance has not been tested on a large dataset of non-demand forecas...
Article
Remote access for Small and Medium Enterprises (SMEs) to group expertise is a key service for efficient decision support. E-tools that could provide such services would enhance the decision-making process and at the same time provide a value-for-money solution; also they are very easy to assemble. E-tools reserve very limited time of their daily wo...
Article
The design of a management information system (MIS) for the purposes of a defence industry could be rather difficult and complex as the managed data are of prime importance for the strategic planning of the country. Therefore, a large amount of parameters should be considered in order to build a solid and proof system with a specific security polic...
Article
The Theta model created a lot of interest in academic circles due to its surprising performance in the M3-competition. However, this interest was not followed by a large number of studies, with the exception of Hyndman and Billah in 2003. The present study discusses the advances in the model that have been made in the last five years and attempts t...
Article
Forecasting the returns from investments in mutual funds is a very difficult problem. This study examines a new forecasting approach and system for the performance of mutual funds in Greece. This is accomplished via an application of a variation of the Theta model on a time series composed of the daily values of mutual funds. The proposed models ar...