Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

Mohammadipour, Maryam; Boylan, John E.

doi:10.1016/j.omega.2011.08.008

Cited by 33 publications

(11 citation statements)

References 38 publications

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“…Brannas et al first studied the nonoverlapping temporal aggregation of an integer autoregressive process of order one, INAR(1), they have shown that the aggregated series follows an integer autoregressive moving average process of order one, INARMA (1,1). Mohammadipour and Boylan have studied theoretically the effects of overlapping temporal aggregation of INARMA processes. They showed that the aggregation of an INARMA process over a given horizon results in an INARMA process as well.…”

Section: Literature Reviewmentioning

confidence: 99%

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Demand forecasting by temporal aggregation

Rostami-Tabar

Babaï

Syntetos

et al. 2013

Naval Research Logistics

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Demand forecasting performance is subject to the uncertainty underlying the time series an organisation is dealing with. There are many approaches that may be used to reduce uncertainty and thus to improve forecasting performance. One intuitively appealing such approach is to aggregate demand in lowerfrequency 'time buckets'. The approach under concern is termed to as Temporal Aggregation and in this paper we investigate its impact on forecasting performance. We assume that the non-aggregated demand follows either a moving average process of order one or a first-order autoregressive process and a Single Exponential Smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical Mean Squared Error expressions are derived for the aggregated and non-aggregated demand in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant and the process parameters. Valuable insights are offered to practitioners and the paper closes with an agenda for further research in this area.

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Section: Literature Reviewmentioning

confidence: 99%

“…Mohammadipour and Boylan 2 http://www.neural-forecasting-competition.com/NN5/. [21] have studied theoretically the effects of overlapping temporal aggregation of INARMA processes. They showed that the aggregation of an INARMA process over a given horizon results in an INARMA process as well.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand forecasting by temporal aggregation

Rostami-Tabar

Babaï

Syntetos

et al. 2013

Naval Research Logistics

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“…Among others, Mohammadipour and Boylan (2012) have analysed the theoretical and empirical forecasting outperformance of overlapping temporal aggregation under Integer ARMA processes. Porras and Dekker (2008) have shown good stock control performance for overlapping temporal aggregation, based on an empirical investigation conducted with a Dutch petrochemical complex.…”

Section: Introductionmentioning

confidence: 99%

On the performance of overlapping and non-overlapping temporal demand aggregation approaches

Boylan

Babaï

2016

International Journal of Production Economics

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“…In high-frequency sampling short-run dynamics are prominent, and as the sampling frequency decreases long-run characteristics such as a trend and seasonality are observed. In this way, different time series characteristics can be identified and the combined results can be more accurate in forecasting and bias reduction (Abraham, 1982;Mohammadipour and Boylan, 2012).…”

Section: Methodology Overviewmentioning

confidence: 99%

Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

Plakandaras

Papadimitriou

Gogas

2015

Journal of Forecasting

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In this paper we propose and test a forecasting model on monthly and daily spot prices of five selected exchange rates. In doing so, we combine a novel smoothing technique (initially applied in signal processing) with a variable selection methodology and two regression estimation methodologies from the field of machine learning (ML). After the decomposition of the original exchange rate series using an ensemble empirical mode decomposition (EEMD) method into a smoothed and a fluctuation component, multivariate adaptive regression splines (MARS) are used to select the most appropriate variable set from a large set of explanatory variables that we collected. The selected variables are then fed into two distinctive support vector machines (SVR) models that produce one‐period‐ahead forecasts for the two components. Neural networks (NN) are also considered as an alternative to SVR. The sum of the two forecast components is the final forecast of the proposed scheme. We show that the above implementation exhibits a superior in‐sample and out‐of‐sample forecasting ability when compared to alternative forecasting models. The empirical results provide evidence against the efficient market hypothesis for the selected foreign exchange markets. Copyright © 2015 John Wiley & Sons, Ltd.

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Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

Cited by 33 publications

References 38 publications

Demand forecasting by temporal aggregation

Demand forecasting by temporal aggregation

On the performance of overlapping and non-overlapping temporal demand aggregation approaches

Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

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