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Forecasting with Temporal Hierarchies

Research output: Working paper

Unpublished

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Forecasting with Temporal Hierarchies. / Athanasopoulos, George; Hyndman, Rob J.; Kourentzes, Nikos; Petropoulos, Fotios.

Lancaster : Department of Management Science, Lancaster University, 2015.

Research output: Working paper

Harvard

Athanasopoulos, G, Hyndman, RJ, Kourentzes, N & Petropoulos, F 2015 'Forecasting with Temporal Hierarchies' Department of Management Science, Lancaster University, Lancaster.

APA

Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2015). Forecasting with Temporal Hierarchies. Department of Management Science, Lancaster University.

Vancouver

Athanasopoulos G, Hyndman RJ, Kourentzes N, Petropoulos F. Forecasting with Temporal Hierarchies. Lancaster: Department of Management Science, Lancaster University. 2015.

Author

Athanasopoulos, George ; Hyndman, Rob J. ; Kourentzes, Nikos ; Petropoulos, Fotios. / Forecasting with Temporal Hierarchies. Lancaster : Department of Management Science, Lancaster University, 2015.

Bibtex

@techreport{b8d30a4849af4b079f5560c304830440,
title = "Forecasting with Temporal Hierarchies",
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.",
author = "George Athanasopoulos and Hyndman, {Rob J.} and Nikos Kourentzes and Fotios Petropoulos",
year = "2015",
language = "English",
publisher = "Department of Management Science, Lancaster University",
type = "WorkingPaper",
institution = "Department of Management Science, Lancaster University",

}

RIS

TY - UNPB

T1 - Forecasting with Temporal Hierarchies

AU - Athanasopoulos, George

AU - Hyndman, Rob J.

AU - Kourentzes, Nikos

AU - Petropoulos, Fotios

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

M3 - Working paper

BT - Forecasting with Temporal Hierarchies

PB - Department of Management Science, Lancaster University

CY - Lancaster

ER -