| 1. |
- Athanasopoulos, George, et al.
(författare)
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Probabilistic Forecasts Using Expert Judgment : The Road to Recovery From COVID-19
- 2023
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Ingår i: Journal of Travel Research. - : Sage Publications. - 0047-2875 .- 1552-6763. ; 62:1, s. 233-258
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Tidskriftsartikel (refereegranskat)abstract
- 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. Our empirical application focuses on Australia, analyzing international arrivals and domestic flows. Both sectors have been severely affected by travel restrictions in the form of international and interstate border closures and regional lockdowns. The two sets of forecasts, allow policy makers to map out the road to recovery and also estimate the expected effect of the pandemic.
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| 2. |
- Kourentzes, Nikolaos, et al.
(författare)
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Cross-temporal coherent forecasts for Australian tourism
- 2019
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Ingår i: Annals of Tourism Research. - : Elsevier. - 0160-7383 .- 1873-7722. ; 75, s. 393-409
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Tidskriftsartikel (refereegranskat)abstract
- Key to ensuring a successful tourism sector is timely policy making and detailed planning. National policy formulation and strategic planning requires long-term forecasts at an aggregate level, while regional operational decisions require short-term forecasts, relevant to local tourism operators. For aligned decisions at all levels, supporting forecasts must be ‘coherent’ that is they should add up appropriately, across relevant demarcations (e.g., geographical divisions or market segments) and also across time. We propose an approach for generating coherent forecasts across both cross-sections and planning horizons for Australia. This results in significant improvements in forecast accuracy with substantial decision making benefits. Coherent forecasts help break intra- and inter-organisational information and planning silos, in a data driven fashion, blending information from different sources. This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecast, a special selection of research in this field.
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| 3. |
- Kourentzes, Nikolaos, et al.
(författare)
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Visitor arrivals forecasts amid COVID-19 : A perspective from the Africa team
- 2021
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Ingår i: Annals of Tourism Research. - : Elsevier. - 0160-7383 .- 1873-7722. ; 88
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Tidskriftsartikel (refereegranskat)abstract
- COVID-19 disrupted international tourism worldwide, subsequently presenting forecasters with a challenging conundrum. In this competition, we predict international arrivals for 20 destinations in two phases: (i) Ex post forecasts pre-COVID; (ii) Ex ante forecasts during and after the pandemic up to end 2021. Our results show that univariate combined with cross-sectional hierarchical forecasting techniques (THieF-ETS) outperform multivariate models pre-COVID. Scenarios were developed based on judgemental adjustment of the THieF-ETS baseline forecasts. Analysts provided a regional view on the most likely path to normal, based on country-specific regulations, macroeconomic conditions, seasonal factors and vaccine development. Results show an average recovery of 58% compared to 2019 tourist arrivals in the 20 destinations under the medium scenario; severe, it is 34% and mild, 80%.
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| 4. |
- Athanasopoulos, George, et al.
(författare)
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Forecasting with temporal hierarchies
- 2017
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Ingår i: European Journal of Operational Research. - : Elsevier. - 0377-2217 .- 1872-6860. ; 262:1, s. 60-74
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Tidskriftsartikel (refereegranskat)abstract
- 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 forecasts. The implied combination mitigates modelling uncertainty, while the reconciled nature of the forecasts results in a unified prediction that supports aligned decisions at different planning horizons: from short-term operational up to long-term strategic planning. The proposed methodology is independent of forecasting models. It can embed high level managerial forecasts that incorporate complex and unstructured information with lower level statistical forecasts. Our results show that forecasting with temporal hierarchies increases accuracy over conventional forecasting, particularly under increased modelling uncertainty. We discuss organisational implications of the temporally reconciled forecasts using a case study of Accident & Emergency departments.
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| 5. |
- Barrow, Devon, et al.
(författare)
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Automatic robust estimation for exponential smoothing : Perspectives from statistics and machine learning
- 2020
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 160
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Tidskriftsartikel (refereegranskat)abstract
- A major challenge in automating the production of a large number of forecasts, as often required in many business applications, is the need for robust and reliable predictions. Increased noise, outliers and structural changes in the series, all too common in practice, can severely affect the quality of forecasting. We investigate ways to increase the reliability of exponential smoothing forecasts, the most widely used family of forecasting models in business forecasting. We consider two alternative sets of approaches, one stemming from statistics and one from machine learning. To this end, we adapt M-estimators, boosting and inverse boosting to parameter estimation for exponential smoothing. We propose appropriate modifications that are necessary for time series forecasting while aiming to obtain scalable algorithms. We evaluate the various estimation methods using multiple real datasets and find that several approaches outperform the widely used maximum likelihood estimation. The novelty of this work lies in (1) demonstrating the usefulness of M-estimators, (2) and of inverse boosting, which outperforms standard boosting approaches, and (3) a comparative look at statistics versus machine learning inspired approaches.
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| 6. |
- Barrow, Devon, et al.
(författare)
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Developing Personalised Learning Support for the Business Forecasting Curriculum : The Forecasting Intelligent Tutoring System
- 2024
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Ingår i: Forecasting. - : MDPI. - 2571-9394 .- 2571-9394. ; 6:1, s. 204-223
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Tidskriftsartikel (refereegranskat)abstract
- In forecasting research, the focus has largely been on decision support systems for enhancing performance, with fewer studies in learning support systems. As a remedy, Intelligent Tutoring Systems (ITSs) offer an innovative solution in that they provide one-on-one online computer-based learning support affording student modelling, adaptive pedagogical response, and performance tracking. This study provides a detailed description of the design and development of the first Forecasting Intelligent Tutoring System, aptly coined FITS, designed to assist students in developing an understanding of time series forecasting using classical time series decomposition. The system’s impact on learning is assessed through a pilot evaluation study, and its usefulness in understanding how students learn is illustrated through the exploration and statistical analysis of a small sample of student models. Practical reflections on the system’s development are also provided to better understand how such systems can facilitate and improve forecasting performance through training.
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| 7. |
- Barrow, Devon K., et al.
(författare)
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Distributions of forecasting errors of forecast combinations : Implications for inventory management
- 2016
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Ingår i: International Journal of Production Economics. - : Elsevier. - 0925-5273 .- 1873-7579. ; 177, s. 24-33
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Tidskriftsartikel (refereegranskat)abstract
- Inventory control systems rely on accurate and robust forecasts of future demand to support decisions such as setting of safety stocks. The combination of multiple forecasts is shown to be effective not only in reducing forecast errors, but also in being less sensitive to limitations of a single model. Research on forecast combination has primarily focused on improving accuracy, largely ignoring the overall shape and distribution of forecast errors. Nonetheless, these are essential for managing the level of aversion to risk and uncertainty for companies. This study examines the forecast error distributions of base and combination forecasts and their implications for inventory performance. It explores whether forecast combinations transform the forecast error distribution towards desired properties for safety stock calculations, typically based on the assumption of normally distributed errors and unbiased forecasts. In addition, it considers the similarity between in- and out-of-sample characteristics of such errors and the impact of different lead times. The effects of established combination methods are explored empirically using a representative set of forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care UK manufacturer. Findings suggest that forecast combinations make the in- and out-of-sample behaviour more consistent, requiring less safety stock on average than base forecasts. Furthermore we find that using in-sample empirical error distributions of combined forecasts approximates well the out-of-sample ones, in contrast to base forecasts.
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| 8. |
- Kourentzes, Nikolaos
(författare)
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Demand Forecasting for Managers
- 2018
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 34:1, s. 117-118
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Recension (övrigt vetenskapligt/konstnärligt)
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| 9. |
- Kourentzes, Nikolaos, et al.
(författare)
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Incorporating Leading Indicators into Sales Forecasts
- 2018
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Ingår i: Foresight: The International Journal of Applied Forecasting. - : International Institute of Forecasters. - 1555-9068. ; :48, s. 24-30
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Tidskriftsartikel (refereegranskat)abstract
- Using leading indicators for business forecasting-in contrast to macroeconomic forecasting-has been relatively rare, partly because our traditional time-series methods do not readily allow incorporation of external variables. Nowadays, however, we have an abundance of potentially useful indicators, and there is evidence that utilizing relevant ones in a forecasting model can significantly improve forecast accuracy and transparency. In this article, Nikolaos and Yves show how to find appropriate leading indicators and make good use of them for sales forecasting.
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| 10. |
- Kourentzes, Nikolaos, et al.
(författare)
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Optimising forecasting models for inventory planning
- 2020
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Ingår i: International Journal of Production Economics. - : Elsevier. - 0925-5273 .- 1873-7579. ; 225
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Tidskriftsartikel (refereegranskat)abstract
- Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter.
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