A Comparison of Various Forecasting Methods for Autocorrelated Time Series

Kandananond, Karin

doi:10.5772/51088

Cited by 31 publications

(10 citation statements)

References 11 publications

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“…The approach consisted of a combination of discrete wavelet theory, ANNs and an adaptive-network-based fuzzy inference system. Further studies offered a comparison of ANN with more traditional tools for demand forecasting; to be more precise, Kandananond (2012) and Shahrabi et al (2009) compared ANNs with ARIMA models, moving average, exponential smoothing and exponential smoothing with trend. Benkachcha et al (2014) and Lu et al (2012) have shown that ANN models provide more accurate forecasting compared to multiple linear regression and other AI tools.…”

Section: Imds 1194mentioning

confidence: 99%

Modelling wholesale distribution operations: an artificial intelligence framework

Bottani

Centobelli

Gallo

et al. 2019

IMDS

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Purpose The purpose of this paper is to propose an artificial intelligence-based framework to support decision making in wholesale distribution, with the aim to limit wholesaler out-of-stocks (OOSs) by jointly formulating price policies and forecasting retailer’s demand. Design/methodology/approach The framework is based on the cascade implementation of two artificial neural networks (ANNs) connected in series. The first ANN is used to derive the selling price of the products offered by the wholesaler. This represents one of the inputs of the second ANN that is used to anticipate the retailer’s demand. Both the ANNs make use of several other input parameters and are trained and tested on a real wholesale supply chain. Findings The application of the ANN framework to a real wholesale supply chain shows that the proposed methodology has the potential to decrease economic loss due to OOS occurrence by more than 56 percent. Originality/value The combined use of ANNs is a novelty in supply chain operation management. Moreover, this approach provides wholesalers with an effective tool to issue purchase orders according to more dependable demand forecasts.

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Section: Imds 1194mentioning

confidence: 99%

Modelling wholesale distribution operations: an artificial intelligence framework

Bottani

Centobelli

Gallo

et al. 2019

IMDS

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“…A survey of predicting stock market returns showed that non-linear models are better for forecasting the returns in emerging and frontier markets [9]. For predicting the market demand [10] and also improving the academic performance of the institution [11], the ANN model gives better accuracy as compared with other advanced models [12]. In a study on predicting the electricity demand in Thailand, the ANN model showed a more significant prediction [13].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

Selvakumar¹,

Satpathi²,

Kumar³

et al. 2021

ujaf

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The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting third party claim amount for different categories of vehicles based on the chosen characteristics of the data. In actuarial research, predicting the insurance claim amount for different vehicle categories is a challenging task, and minimal empirical research studies were done to forecast the claims. In the present study, the annual time series historical data were collected for a period of 34 years. We had built the machine learning predictive models to modeling the claim amount with different categories of vehicles effectively. In this context, we exhibited the feasibility of using a statistical machine learning approach such as Linear regression Model, the Exponential Smoothing Model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict the various categories of vehicles claim amount. The data were analyzed, compared, and the empirical analysis showed that Artificial Neural Network is a better predictive model among the other time series models based on performance evaluation metrics RMSE and MAPE with lesser variance. Therefore, the machine learning approach for forecasting third party claim amounts will help the Insurance Companies in India to provide a better predictive model, which ensures better claims settlement and management for different categories of vehicles.

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“…The two methods are used to forecast the failure of the system. 8 Aburto and Weber 9 combined the two forecasting methods which are ARIMA and neural networks. The efficiency of the hybrid model is compared with traditional forecasting methods.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of demand using ARIMA model

Fattah

Ezzine

Aman

et al. 2018

International Journal of Engineering Business Management

294

109

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The work presented in this article constitutes a contribution to modeling and forecasting the demand in a food company, by using time series approach. Our work demonstrates how the historical demand data could be utilized to forecast future demand and how these forecasts affect the supply chain. The historical demand information was used to develop several autoregressive integrated moving average (ARIMA) models by using Box-Jenkins time series procedure and the adequate model was selected according to four performance criteria: Akaike criterion, Schwarz Bayesian criterion, maximum likelihood, and standard error. The selected model corresponded to the ARIMA (1, 0, 1) and it was validated by another historical demand information under the same conditions. The results obtained prove that the model could be utilized to model and forecast the future demand in this food manufacturing. These results will provide to managers of this manufacturing reliable guidelines in making decisions.

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A Comparison of Various Forecasting Methods for Autocorrelated Time Series

Cited by 31 publications

References 11 publications

Modelling wholesale distribution operations: an artificial intelligence framework

Modelling wholesale distribution operations: an artificial intelligence framework

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

Forecasting of demand using ARIMA model

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