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Article
Publication date: 4 September 2020

Mehdi Khashei and Bahareh Mahdavi Sharif

The purpose of this paper is to propose a comprehensive version of a hybrid autoregressive integrated moving average (ARIMA), and artificial neural networks (ANNs) in…

Abstract

Purpose

The purpose of this paper is to propose a comprehensive version of a hybrid autoregressive integrated moving average (ARIMA), and artificial neural networks (ANNs) in order to yield a more general and more accurate hybrid model for exchange rates forecasting. For this purpose, the Kalman filter technique is used in the proposed model to preprocess and detect the trend of raw data. It is basically done to reduce the existing noise in the underlying data and better modeling, respectively.

Design/methodology/approach

In this paper, ARIMA models are applied to construct a new hybrid model to overcome the above-mentioned limitations of ANNs and to yield a more general and more accurate model than traditional hybrid ARIMA and ANNs models. In our proposed model, a time series is considered as a function of a linear and nonlinear component, so, in the first phase, an ARIMA model is first used to identify and magnify the existing linear structures in data. In the second phase, a multilayer perceptron is used as a nonlinear neural network to model the preprocessed data, in which the existing linear structures are identified and magnified by ARIMA and to predict the future value of time series.

Findings

In this paper, a new Kalman filter based hybrid artificial neural network and ARIMA model are proposed as an alternate forecasting technique to the traditional hybrid ARIMA/ANNs models. In the proposed model, similar to the traditional hybrid ARIMA/ANNs models, the unique strengths of ARIMA and ANN in linear and nonlinear modeling are jointly used, aiming to capture different forms of relationship in the data; especially, in complex problems that have both linear and nonlinear correlation structures. However, there are no aforementioned assumptions in the modeling process of the proposed model. Therefore, in the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be generally guaranteed that the performance of the proposed model will not be worse than either of their components used separately. In addition, empirical results in both weekly and daily exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models.

Originality/value

In the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be guaranteed that the performance of the proposed model will not be worse than either of the components used separately. In addition, empirical results in exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Therefore, it can be used as an appropriate alternate model for forecasting in exchange ratemarkets, especially when higher forecasting accuracy is needed.

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Article
Publication date: 2 March 2015

Arvydas Jadevicius and Simon Huston

This paper aims to investigate Lithuanian house price changes. Its twin motivations are the importance of information on future house price movements to sector…

Abstract

Purpose

This paper aims to investigate Lithuanian house price changes. Its twin motivations are the importance of information on future house price movements to sector stakeholders and the limited number of related Lithuanian property market studies.

Design/methodology/approach

The study employs ARIMA modelling approach. It assesses whether past is a good predictor of the future. It then examines issues relating to an application of this univariate time-series modelling technique in a forecasting context.

Findings

As the results of the study suggest, ARIMA is a useful technique to assess broad market price changes. Government and central bank can use ARIMA modelling approach to forecast national house price inflation. Developers can employ this methodology to drive successful house-building programme. Investor can incorporate forecasts from ARIMA models into investment strategy for timing purposes.

Research limitations/implications

Certainly, there are number of limitations attached to this particular modelling approach. Firm predictions about house price movements are also a challenge, as well as more research needs to be done in establishing a dynamic interrelationship between macro variables and the Lithuanian housing market.

Originality/value

Although the research focused on Lithuania, the findings extend to global housing market. ARIMA house price modelling provides insights for a spectrum of stakeholders. The use of this modelling approach can be employed to improve monetary policy oversight, facilitate planning for infrastructure or social housing as a countercyclical policy and mitigate risk for investors. What is more, a greater appreciation of Lithuania housing market can act as a bellwether for real estate markets in other trade-exposed small country economies.

Details

International Journal of Housing Markets and Analysis, vol. 8 no. 1
Type: Research Article
ISSN: 1753-8270

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Article
Publication date: 20 October 2021

Yanhui Song and Jiayi Cao

The purpose of this paper is to predict bibliometric indicators based on ARIMA models and to study the short-term trends of bibliometric indicators.

Abstract

Purpose

The purpose of this paper is to predict bibliometric indicators based on ARIMA models and to study the short-term trends of bibliometric indicators.

Design/methodology/approach

This paper establishes a non-stationary time series ARIMA (p, d, q) model for forecasting based on the bibliometric index data of 13 journals in the library intelligence category selected from the Chinese Social Sciences Citation Index (CSSCI) as the data source database for the period 1998–2018, and uses ACF and PACF methods for parameter estimation to predict the development trend of the bibliometric index in the next 5 years. The predicted model was also subjected to error analysis.

Findings

ARIMA models are feasible for predicting bibliometric indicators. The model predicted the trend of the four bibliometric indicators in the next 5 years, in which the number of publications showed a decreasing trend and the H-value, average citations and citations showed an increasing trend. Error analysis of the model data showed that the average absolute percentage error of the four bibliometric indicators was within 5%, indicating that the model predicted well.

Research limitations/implications

This study has some limitations. 13 Chinese journals were selected in the field of Library and Information Science as the research objects. However, the scope of research based on bibliometric indicators of Chinese journals is relatively small and cannot represent the evolution trend of the entire discipline. Therefore, in the future, the authors will select different fields and different sources for further research.

Originality/value

This study predicts the trend changes of bibliometric indicators in the next 5 years to understand the trend of bibliometric indicators, which is beneficial for further in-depth research. At the same time, it provides a new and effective method for predicting bibliometric indicators.

Details

Aslib Journal of Information Management, vol. 74 no. 1
Type: Research Article
ISSN: 2050-3806

Keywords

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Article
Publication date: 15 July 2021

Kathiresh Mayilsamy, Maideen Abdhulkader Jeylani A,, Mahaboob Subahani Akbarali and Haripranesh Sathiyanarayanan

The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for…

Abstract

Purpose

The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series.

Design/methodology/approach

Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity.

Findings

The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads.

Originality/value

The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 40 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 September 2005

Lawrence Chin and Gang‐Zhi Fan

The purpose of this paper is to examine the nature of Singapore's private housing market with respect to its price movement using time series models.

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Abstract

Purpose

The purpose of this paper is to examine the nature of Singapore's private housing market with respect to its price movement using time series models.

Design/methodology/approach

This paper analyses the price dynamics in the Singapore private housing market using the integrated autoregressive‐moving average modeling coupled with outlier detection and autoregressive conditional heteroskedasticity modeling techniques.

Findings

The paper finds that private house prices are better modeled as an ARIMA (1, 1, 0) model with corresponding dummy variables. This suggests that housing prices may be characterized as the combination of a stationary cyclical component and a non‐stationary stochastic growth component over the past almost three decades. This affirms that the Singapore's private housing market is characterised by the weak‐form inefficiency.

Research limitations/implications

The results show that even though ARIMA with dummy variables performs better to ARIMA with ARCH in dynamic performance, there is only marginal improvement on the original model. This suggests that the method for selecting intervention variables in the ARIMA modeling is worth further research with the aim of improving its predictive ability.

Originality/value

This paper incorporates the detection of outliers and intervention procedure in the modeling in order to analyse the impacts of extraordinary events such the recent Asian financial crisis and excessive market speculation on property prices and take them into consideration in forecasting price changes.

Details

Property Management, vol. 23 no. 4
Type: Research Article
ISSN: 0263-7472

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Article
Publication date: 1 May 2007

Simon Stevenson

ARIMA models have been extensively examined in the context of the real estate market. The purpose of this paper is to examine issues relating to their application in a…

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Abstract

Purpose

ARIMA models have been extensively examined in the context of the real estate market. The purpose of this paper is to examine issues relating to their application in a forecasting context. Specifically, the paper seeks to examine whether in‐sample measures of best‐fit and also past forecasting accuracy bear any relation to future forecasting performance.

Design/methodology/approach

The forecasting performance of alternative ARIMA specifications are compared over rolling estimation and forecasting windows. The forecasting accuracy of the alternative specifications is compared with specific attention placed on the accuracy of the respective specification that in‐sample provides the best fitting model.

Findings

The results highlight the limitations in using the conventional approach to identifying the best‐specified ARIMA model in sample, when the purpose of the analysis is to provide forecasts. The results show that while ARIMA models can be useful in anticipating broad market trends, there are substantial differences in the forecasts obtained using alternative specifications. The use of conventional measures of best‐fit provide little indication as to future forecasting ability, nor does the forecasting performance of a specification in previous periods.

Originality/value

ARIMA modelling has frequently been highlighted as a useful forecasting approach. This paper illustrates that care needs to be paid in their use in a forecasting context and full appreciation of the strengths and limitations of the ARIMA approach.

Details

Journal of Property Investment & Finance, vol. 25 no. 3
Type: Research Article
ISSN: 1463-578X

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Article
Publication date: 1 December 2006

Mohammad Al‐Shiab

This study examines the univariate ARIMA forecasting model, using the Amman Stock Exchange (ASE) general daily index between 4/1/2004 and 10/8/2004; with out‐of‐sample…

Abstract

This study examines the univariate ARIMA forecasting model, using the Amman Stock Exchange (ASE) general daily index between 4/1/2004 and 10/8/2004; with out‐of‐sample testing undertaken on the following seven days. Different diagnostic tests were performed to find the best model describing the data. The selected model predicted that the ASE would continue to grow by 0.195% for seven days starting on 11/8/2004. This forecast, however, was not consistent with actual performance during the period of the prediction (11/8/2004 ‐ 19/8/2004) since ASE declined by ‐ 0.003% assuring the fact that ASE followed most closely the Efficient Market Hypothesis (EMH) in its weak form.

Details

Journal of Economic and Administrative Sciences, vol. 22 no. 2
Type: Research Article
ISSN: 1026-4116

Keywords

Open Access
Article
Publication date: 12 June 2017

Nara Rossetti, Marcelo Seido Nagano and Jorge Luis Faria Meirelles

This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany…

Abstract

Purpose

This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market.

Design/methodology/approach

To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive conditional heteroscedasticity (EGARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and periodic generalized autoregressive conditional heteroscedasticity (PGARCH), and a combination of these with autoregressive integrated moving average (ARIMA) models, checking which of these processes were more efficient in capturing volatility of interest rates of each of the sample countries.

Findings

The results suggest that for most markets, studied volatility is best modelled by asymmetric GARCH processes – in this case the EGARCH – demonstrating that bad news leads to a higher increase in the volatility of these markets than good news. In addition, the causes of increased volatility seem to be more associated with events occurring internally in each country, as changes in macroeconomic policies, than the overall external events.

Originality/value

It is expected that this study has contributed to a better understanding of the volatility of interest rates and the main factors affecting this market.

Propósito

Este estudio analiza la volatilidad del mercado de renta fija de once países (Brasil, Rusia, India, China, Sudáfrica, Argentina, Chile, México, Estados Unidos, Alemania y Japón) de enero de 2000 a diciembre de 2011, mediante el examen de las tasas de interés interbancarias de cada mercado.

Diseño/metodología/enfoque

Para la volatilidad de los retornos de las tasas de interés, se utilizaron modelos de heteroscedasticidad condicional autorregresiva: ARCH, GARCH, EGARCH, TGARCH y PGARCH, y una combinación de estos con modelos ARIMA, comprobando cuáles de los procesos eran más eficientes para capturar la volatilidad de interés de cada uno de los países de la muestra.

Hallazgos

Los resultados sugieren que para la mayoría de los mercados estudiados la volatilidad es mejor modelada por procesos GARCH asimétricos —en este caso el EGARCH— demostrando que las malas noticias conducen a un mayor incremento en la volatilidad de estos mercados que las buenas noticias. Además, las causas de una mayor volatilidad parecen estar más asociadas a eventos que ocurren internamente en cada país, como cambios en las políticas macroeconómicas, que los eventos externos generales.

Originalidad/valor

Se espera que este estudio contribuya a un mejor entendimiento de la volatilidad de las tasas de interés y de los principales factores que afectan a este mercado.

Palabras clave

Ingreso fijo, Volatilidad, Países emergentes, Modelos ARCH-GARCH

Tipo de artículo

Artículo de investigación

Details

Journal of Economics, Finance and Administrative Science, vol. 22 no. 42
Type: Research Article
ISSN: 2077-1886

Keywords

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Article
Publication date: 23 November 2012

Faruk Balli and Elsayed Mousa Elsamadisy

This paper seeks to model the daily and weekly forecasting of the currency in circulation (CIC) for the State of Qatar.

Abstract

Purpose

This paper seeks to model the daily and weekly forecasting of the currency in circulation (CIC) for the State of Qatar.

Design/methodology/approach

The paper employs linear forecasting models, the regression model and the seasonal ARIMA model to forecast the CIC for Qatar.

Findings

Comparing the linear methods, the seasonal ARIMA model provides better estimates for short‐term forecasts. The range of forecast errors for the seasonal ARIMA model forecasts are less than 100 million QR for the short‐term CIC forecasts.

Practical implications

The findings of this paper suggest that the CIC in Qatar is in a pattern and it would be easier to forecast the currency in circulation in Qatar economy. Accurate estimates of money market liquidity would help Qatar Central bank, to maintain the price stability in the Qatar economy.

Originality/value

This paper forecasts the currency in circulation for the State of Qatar. Additionally, the empirical part of the paper compares the different methodologies find the appropriate model for the CIC for the state of Qatar.

Details

International Journal of Islamic and Middle Eastern Finance and Management, vol. 5 no. 4
Type: Research Article
ISSN: 1753-8394

Keywords

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Book part
Publication date: 18 July 2016

Matthew Lindsey and Robert Pavur

Research in the area of forecasting and stock inventory control for intermittent demand is designed to provide robust models for the underlying demand which appears at…

Abstract

Research in the area of forecasting and stock inventory control for intermittent demand is designed to provide robust models for the underlying demand which appears at random, with some time periods having no demand at all. Croston’s method is a popular technique for these models and it uses two single exponential smoothing (SES) models which involve smoothing constants. A key issue is the choice of the values due to the sensitivity of the forecasts to changes in demand. Suggested selections of the smoothing constants include values between 0.1 and 0.3. Since an ARIMA model has been illustrated to be equivalent to SES, an optimal smoothing constant can be selected from the ARIMA model for SES. This chapter will conduct simulations to investigate whether using an optimal smoothing constant versus the suggested smoothing constant is important. Since SES is designed to be an adapted method, data are simulated which vary between slow and fast demand.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78635-534-8

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