A nearest neighbor model for forecasting market response

Mulhern, Francis; Caprara, Robert J.

doi:10.1016/0169-2070(94)90002-7

Cited by 42 publications

(12 citation statements)

References 27 publications

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“…The NPR method has been developed in the past two decades and has been applied to areas such as hydrology (Yakowitz, 1987) and marketing (Mulhern and Caprara, 1994). Davis and Nihan (1991) showed that the NPR method can also be used for short-term traffic forecasts.…”

Section: Non-parametric Regression (Npr)mentioning

confidence: 99%

Comparison of two non‐parametric models for daily traffic forecasting in Hong Kong

Lam

Tang

Tam

2006

Journal of Forecasting

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The most up-to-date annual average daily traffic (AADT) is always required for transport model development and calibration. However, the current-year AADT data are not always available. The short-term traffic flow forecasting models can be used to predict the traffic flows for the current year. In this paper, two non-parametric models, non-parametric regression (NPR) and Gaussian maximum likelihood (GML), are chosen for short-term traffic forecasting based on historical data collected for the annual traffic census (ATC) in Hong Kong. These models are adapted as they are more flexible and efficient in forecasting the daily vehicular flows in the Hong Kong ATC core stations (in total of 87 stations). The daily vehicular flows predicted by these models are then used to calculate the AADT of the current year, 1999. The overall prediction and comparison results show that the NPR model produces better forecasts than the GML model using the ATC data in Hong Kong. Copyright © 2006 John Wiley _ Sons, Ltd.

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Section: Non-parametric Regression (Npr)mentioning

confidence: 99%

Comparison of two non‐parametric models for daily traffic forecasting in Hong Kong

Lam

Tang

Tam

2006

Journal of Forecasting

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“…The attractor of the chaotic system is the value to which the system settles when time approaches infinity. This occurs as the kNN approach tries to rebuild the attractor of the process that generates the time series [37] and the average of past values puts each instance on the cyclic pattern of the attractor. Various state spaces are investigated, and Section 4 shows the results.…”

Section: Knn Non-parametric Regressionmentioning

confidence: 99%

GPS Data-based Non-parametric Regression for Predicting Travel Times in Urban Traffic Networks

Marković¹,

Bašić²,

Gold³

et al. 2012

PROMET

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“…They attribute the superiority of the k-NN model to its ability to identify more complex interactions across the input variables and filter out more noise in the observations. In another example from marketing literature Mulhern and Caprara (1994) combine a multivariate NN model with regression analysis to forecast market response, using store scanner data for a consumer packaged good. Their results suggest that such an approach has obvious advantages versus the more traditional Box-Jenkins analysis as it allows time effects (traditionally filtered out in ARIMA models) to be integrated into the causal relationships.…”

Section: Analytics (Marketing Logistics)mentioning

confidence: 99%

Forecasting supply chain sporadic demand with nearest neighbor approaches

Νικολόπουλος

Babaï

Bozos

2016

International Journal of Production Economics

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One of the biggest challenges in Supply Chain Management (SCM) is to forecast sporadic demand. Our forecasting methods arsenal includes Croston s method, SBA and TSB as well as some more recent non-parametric advances, but none of these can identify and extrapolate patterns existing in data; this is essential as these patterns do appear quite often, driven by infrequent but nevertheless repetitive managerial practices. One could claim such patterns could be picked up by Artificial Intelligence approaches, however these do need large training datasets, unfortunately non-existent in industrial time series. Nearest Neighbors (NN) can however operate in these latter contexts, and pick up patterns even in short series. In this research we propose applying NN for supply chain data and we investigate the conditions under which these perform adequately through an extensive simulation. Furthermore, via an empirical investigation in automotive data we provide evidence that practitioners could benefit from employing supervised NN approaches. The contribution of this research is not in the development of a new theory, but in the proposition of a new conceptual framework that brings existing theory (i.e. NN) from Computer Science and Statistics and applies it successfully in an SCM setting.

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A nearest neighbor model for forecasting market response

Cited by 42 publications

References 27 publications

Comparison of two non‐parametric models for daily traffic forecasting in Hong Kong

Comparison of two non‐parametric models for daily traffic forecasting in Hong Kong

GPS Data-based Non-parametric Regression for Predicting Travel Times in Urban Traffic Networks

Forecasting supply chain sporadic demand with nearest neighbor approaches

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