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  • 1.
    Athanasopoulos, George
    et al.
    Department of Econometrics and Business Statistics, Monash University, Australia.
    Hyndman, Rob. J.
    Department of Econometrics and Business Statistics, Monash University, Australia.
    Kourentzes, Nikolaos
    epartment of Management Science, Lancaster University Management School, United Kingdom.
    Petropoulos, Fotios
    Information, Decision and Operations Division, School of Management, University of Bath, United Kingdom.
    Forecasting with temporal hierarchies2017In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 262, no 1, p. 60-74Article in journal (Refereed)
    Abstract [en]

    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. 

  • 2.
    Fildes, Robert
    et al.
    Lancaster Centre for Forecasting, Lancaster University, Department of Management Science, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster Centre for Forecasting, Lancaster University, Department of Management Science, United Kingdom.
    Validation and forecasting accuracy in models of climate change2011In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 27, no 4, p. 968-995Article in journal (Refereed)
    Abstract [en]

    Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster's perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.

  • 3.
    Kourentzes, Nikolaos
    et al.
    Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
    Barrow, Devon K.
    Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
    Crone, Sven F.
    Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
    Neural network ensemble operators for time series forecasting2014In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 41, no 9, p. 4235-4244Article in journal (Refereed)
    Abstract [en]

    The combination of forecasts resulting from an ensemble of neural networks has been shown to outperform the use of a single "best" network model. This is supported by an extensive body of literature, which shows that combining generally leads to improvements in forecasting accuracy and robustness, and that using the mean operator often outperforms more complex methods of combining forecasts. This paper proposes a mode ensemble operator based on kernel density estimation, which unlike the mean operator is insensitive to outliers and deviations from normality, and unlike the median operator does not require symmetric distributions. The three operators are compared empirically and the proposed mode ensemble operator is found to produce the most accurate forecasts, followed by the median, while the mean has relatively poor performance. The findings suggest that the mode operator should be considered as an alternative to the mean and median operators in forecasting applications. Experiments indicate that mode ensembles are useful in automating neural network models across a large number of time series, overcoming issues of uncertainty associated with data sampling, the stochasticity of neural network training, and the distribution of the forecasts. 

  • 4.
    Kourentzes, Nikolaos
    et al.
    Department of Management Science, Lancaster University Management School, Lancaster University, United Kingdom.
    Barrow, Devon
    Faculty of Business, Environment and Society, Coventry University, United Kingdom.
    Petropoulos, Fotios
    School of Management, University of Bath, United Kingdom.
    Another look at forecast selection and combination: Evidence from forecast pooling2019In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 209, p. 226-235Article in journal (Refereed)
    Abstract [en]

    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 are appropriate, or not, is typically overlooked and various weighting schemes have been proposed to lessen the impact of inappropriate forecasts. We argue that selecting a reasonable pool of forecasts is fundamental in the modelling process and in this context both forecast selection and combination can be seen as two extreme pools of forecasts. We evaluate forecast pooling approaches and find them beneficial in terms of forecast accuracy. We propose a heuristic to automatically identify forecast pools, irrespective of their source or the performance criteria, and demonstrate that in various conditions it performs at least as good as alternative pools that require additional modelling decisions and better than selection or combination. 

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