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Article
Publication date: 14 July 2021

Maryam Bahrami, Mehdi Khashei and Atefeh Amindoust

The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to…

Abstract

Purpose

The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting.

Design/methodology/approach

The main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components.

Findings

Empirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed.

Originality/value

To the best of the authors’ knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed.

Article
Publication date: 5 July 2022

Xianting Yao and Shuhua Mao

Given the effects of natural and social factors, data on both the supply and demand sides of electricity will produce obvious seasonal fluctuations. The purpose of this article is…

Abstract

Purpose

Given the effects of natural and social factors, data on both the supply and demand sides of electricity will produce obvious seasonal fluctuations. The purpose of this article is to propose a new dynamic seasonal grey model based on PSO-SVR to forecast the production and consumption of electric energy.

Design/methodology/approach

In the model design, firstly, the parameters of the SVR are initially optimized by the PSO algorithm for the estimation of the dynamic seasonal operator. Then, the seasonal fluctuations in the electricity demand data are eliminated using the dynamic seasonal operator. After that, the time series after eliminating of the seasonal fluctuations are used as the training set of the DSGM(1, 1) model, and the corresponding fitted, and predicted values are calculated. Finally, the seasonal reduction is performed to obtain the final prediction results.

Findings

This study found that the electricity supply and demand data have obvious seasonal and nonlinear characteristics. The dynamic seasonal grey model based on PSO-SVR performs significantly better than the comparative model for hourly and monthly data as well as for different time durations, indicating that the model is more accurate and robust in seasonal electricity forecasting.

Originality/value

Considering the seasonal and nonlinear fluctuation characteristics of electricity data. In this paper, a dynamic seasonal grey model based on PSO-SVR is established to predict the consumption and production of electric energy.

Details

Grey Systems: Theory and Application, vol. 13 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 31 October 2018

Chung-Han Ho, Ping-Teng Chang, Kuo-Chen Hung and Kuo-Ping Lin

The purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air…

Abstract

Purpose

The purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).

Design/methodology/approach

The prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.

Findings

Seasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.

Originality/value

This study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

Details

Industrial Management & Data Systems, vol. 119 no. 3
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 1 May 2005

Lee‐Ing Tong and Yi‐Hui Liang

To propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs.

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Abstract

Purpose

To propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs.

Design/methodology/approach

This study proposes a method for analysing and forecasting field failure data for repairable systems. The novel method constructs a predictive model by combining the seasonal autoregressive integrated‐moving average (SARIMA) method and neural network model.

Findings

Current methods for analysing and forecasting field failure data for repairable systems do not consider the seasonal effect in the data. The proposed method can not only analyse the trends and seasonal vibration of the data, but can also forecast the short‐ and long‐term reliability of the system based on only a small amount of historical data.

Research limitations/implications

This study adopts only real failure data from an electronic system to verify the feasibility and effectiveness of the proposed method. Future research may use other product's failure data to verify the proposed method.

Practical implications

Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements.

Originality/value

The proposed method is superior to other prediction techniques in predicting future real failure data.

Details

International Journal of Quality & Reliability Management, vol. 22 no. 4
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 1 April 2022

Qiong Jia, Ying Zhu, Rui Xu, Yubin Zhang and Yihua Zhao

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have…

Abstract

Purpose

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

Design/methodology/approach

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

Findings

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

Practical implications

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

Originality/value

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

Details

Industrial Management & Data Systems, vol. 122 no. 10
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 1 September 2000

T.A. Spedding and K.K. Chan

Discusses the development and evaluation of a forecasting model for inventory management in an advanced technology batch production environment. Traditional forecasting and…

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Abstract

Discusses the development and evaluation of a forecasting model for inventory management in an advanced technology batch production environment. Traditional forecasting and inventory management do not adequately address issues relating to a short life cycle and to non‐seasonal products with a relatively long lead time. Limited historical data (fewer than 100 observations) is also a problem in predicting short‐term dynamic or unstable time series. A Bayesian dynamic linear time series model is proposed as an alternative technique for forecasting demand in a dynamically changing environment. Provides details of the important characteristics and development process of the forecasting model. A case study is then presented to illustrate the application of the model based on data from a multinational company in Singapore. It also compares the Bayesian dynamic linear time series model with a classical forecasting model (auto‐regressive integrated moving average (ARIMA) model).

Details

Integrated Manufacturing Systems, vol. 11 no. 5
Type: Research Article
ISSN: 0957-6061

Keywords

Book part
Publication date: 13 March 2013

Virginia M. Miori, James Algeo, Brian Segulin and Dorothy Cimino Brown

Evaluating pain and discomfort in animals is difficult at best. Veterinarians believe however, that they can establish a proxy for estimating levels of pain and discomfort in…

Abstract

Evaluating pain and discomfort in animals is difficult at best. Veterinarians believe however, that they can establish a proxy for estimating levels of pain and discomfort in canines by observing variations in their activity levels. Sufficient research has been conducted to justify this assertion, but little has been conducted to analyze the volumes of activity data collected. We present the first of a series of analyses aimed at ultimately presenting an effective predictive tool for canine pain and discomfort levels. In this chapter, we perform analyses on a dataset of normal (control) dogs, containing almost 3 million records. The forecasting analyses incorporated multiple polynomial regression models with transcendental transformations and ARIMA models to provide effective determination and prediction of baseline normal canine activity levels.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78190-331-5

Keywords

Article
Publication date: 8 March 2022

Iman Cheratian, Saleh Goltabar and Carla Daniela Calá

During recent years, the long-run relationship between the unemployment rate (UR) and the labor force participation (LFP) rate has been examined in-depth in developed and…

Abstract

Purpose

During recent years, the long-run relationship between the unemployment rate (UR) and the labor force participation (LFP) rate has been examined in-depth in developed and developing economies. This paper aims to explore this relationship for Iranian women in 31 provinces from 2005Q2 to 2019Q1.

Design/methodology/approach

To examine the existence of a long-run relationship between female LFP and UR, the time-series cointegration approach has been used. Furthermore, regarding the low power of the univariate cointegration approach, the authors consider a panel version of the cointegration tests developed by Westerlund.

Findings

Both time-series cointegration tests and panel cointegration test support the unemployment invariance hypothesis for most Iranian provinces, especially the most religious ones. As it implies an invariance to supply side policies, it seems that reducing legal and cultural barriers could be more relevant to decrease female UR and increase LFP than training programs or R&D policies. The present results also suggest that, for this group of regions, a more centralized policy design could be appropriate, instead of a regional one.

Originality/value

This study investigates whether the unemployment invariance hypothesis holds for Iran, which has not been analyzed before for the Iranian labor market. Moreover, the study adopts a regional approach, which takes into account the huge regional differences in Iran.

Details

International Journal of Development Issues, vol. 21 no. 2
Type: Research Article
ISSN: 1446-8956

Keywords

Article
Publication date: 5 March 2018

Hadi Rafiei Darani and Hadi Asghari

The purpose of this paper is to study determining factors of international tourism demand in Middle Eastern countries.

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Abstract

Purpose

The purpose of this paper is to study determining factors of international tourism demand in Middle Eastern countries.

Design/methodology/approach

Panel data pattern is used for data analysis of 1995 to 2013.

Findings

Results indicate variables like trade freedom index and gross domestic product (GDP) have positive and significant impact upon tourism demand of the countries of the region. Purchasing power parity (PPP) and GDP per capita are indicators which affect the tourism demand rate in Middle East negatively.

Originality/value

It is estimated that Middle East region will claim for the bulk of tourist arrivals in following years. Therefore, this study is vital for destination managers to plan for demand in future.

Details

International Journal of Culture, Tourism and Hospitality Research, vol. 12 no. 1
Type: Research Article
ISSN: 1750-6182

Keywords

Book part
Publication date: 6 December 2007

Bert M. Balk

There are two main dimensions in which the performance of a production unit can be assessed. The first is the dimension of time. The basic question here is: how is this or that…

Abstract

There are two main dimensions in which the performance of a production unit can be assessed. The first is the dimension of time. The basic question here is: how is this or that production unit doing over time? Assessing a unit's performance over time is called monitoring. The second dimension is characterized by the question: how is this or that production unit doing relative to other, similar units? To answer this question one needs to specify the reference set of units and one needs sufficient information on each of the members of this set. This activity is usually called benchmarking. A combination of the two dimensions in the setting of a panel is also possible.

Details

Evaluating Hospital Policy and Performance: Contributions from Hospital Policy and Productivity Research
Type: Book
ISBN: 978-0-7623-1453-9

1 – 10 of over 5000