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1 – 10 of 619M. Mary Victoria Florence and E. Priyadarshini
This study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a…
Abstract
Purpose
This study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a critical component of an aero engine and its performance is essential for safe and efficient operation of the engine.
Design/methodology/approach
The study analyzes a data set of gas path performance parameters obtained from a fleet of aero engines. The data is preprocessed and then fitted to ARIMA models to predict the future values of the gas path performance parameters. The performance of the ARIMA models is evaluated using various statistical metrics such as mean absolute error, mean squared error and root mean squared error. The results show that the ARIMA models can accurately predict the gas path performance parameters in aero engines.
Findings
The proposed methodology can be used for real-time monitoring and controlling the gas path performance parameters in aero engines, which can improve the safety and efficiency of the engines. Both the Box-Ljung test and the residual analysis were used to demonstrate that the models for both time series were adequate.
Research limitations/implications
To determine whether or not the two series were stationary, the Augmented Dickey–Fuller unit root test was used in this study. The first-order ARIMA models were selected based on the observed autocorrelation function and partial autocorrelation function.
Originality/value
Further, the authors find that the trend of predicted values and original values are similar and the error between them is small.
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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…
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.
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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 order to…
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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Lydie Myriam Marcelle Amelot, Ushad Subadar Agathee and Yuvraj Sunecher
This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian…
Abstract
Purpose
This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.
Design/methodology/approach
Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.
Findings
The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.
Research limitations/implications
The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.
Originality/value
This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.
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Ali Hepşen and Metin Vatansever
It is important to forecast index series to identify future rises, falls, and turning points in the property market. From the point of this necessity and importance, the main…
Abstract
Purpose
It is important to forecast index series to identify future rises, falls, and turning points in the property market. From the point of this necessity and importance, the main purpose of this paper is to forecast the future trends in Dubai housing market.
Design/methodology/approach
This paper uses the monthly time series of Reidin.com Dubai Residential Property Price Index (DRPPI) data. In order to forecast the future trends in Dubai housing market, Box‐Jenkins autoregressive integrated moving average (ARIMA) forecasting method is utilized.
Findings
The results of the ARIMA modeling clearly indicate that average monthly percentage increase in the Reidin.com DRPPI will be 0.23 percent during the period January 2011‐December 2011. That is a 2.44 percent increase in the index for the same period.
Practical implications
Reidin.com residential property price index is a crucial tool to measure Dubai's real estate market. Based on the current index values or past trend, real estate investors (i.e. developers and constructors) decide to start new projects. Attempts have also been made in the past to forecast index series to identify future rises, falls, and turning points in the property market. The results of this paper would also help government and property investors for creating more effective property management strategies in Dubai.
Originality/value
There is no previous study analyzing the future trends in Dubai housing market. At this point, the paper is the first academic study that identifies this relationship.
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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.
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.
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Bhavya Advani, Anshita Sachan, Udit Kumar Sahu and Ashis Kumar Pradhan
A major concern for policymakers and researchers is to ascertain the movement of price levels and employment rates. Predicting the trends of these variables will assist the…
Abstract
A major concern for policymakers and researchers is to ascertain the movement of price levels and employment rates. Predicting the trends of these variables will assist the government in making policies to stabilize the economy. The objective of this chapter is to forecast the unemployment rate and Consumer Price Index (CPI) for the period 2022 to 2031 for the Indian economy. For this purpose, the authors analyse the prediction capability of the univariate auto-regressive integrated moving average (ARIMA) model and the vector autoregressive (VAR) model. The dataset for India's annual CPI and unemployment rate pertains to a 30-year time period from 1991 to 2021. The result shows that the inflation forecasts derived from the ARIMA model are more precise than that of the VAR model. Whereas, unemployment rate forecasts obtained from the VAR model are more reliable than that of the ARIMA model. It is also observed that predicted unemployment rates hover around 5.7% in the forthcoming years, while the forecasted inflation rate witnesses an increasing trend.
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Adriana AnaMaria Davidescu, Eduard Mihai Manta, Margareta-Stela Florescu, Cristina Maria Geambasu and Catalina Radu
The objective of this chapter is to analyse the performance of the UiPath (PATH) company on the New York Stock Exchange, in the context of the war between Russia and Ukraine, and…
Abstract
Purpose
The objective of this chapter is to analyse the performance of the UiPath (PATH) company on the New York Stock Exchange, in the context of the war between Russia and Ukraine, and to predict the closing price of the PATH stock using autoregressive integrated moving average with (ARIMAX) and without (ARIMA) exogenous variable methods and autoregressive neural networks (NNAR, NNARX).
Need for Study
UiPath has gained a significant reputation in the IT market and has become a point of interest in recent years. However, the current context is marked by an event of international impact, the war between Russia and Ukraine. In this context, this analysis will consider performance from two perspectives: forecasts of the closing price and forecasts of the closing price with an exogenous variable, namely the war between Russia and Ukraine.
Methodology
In the analysis that follows, we will address a forecast of the stock closing price using ARIMA, ARIMAX, NNAR and NNARX, as well as analysis of changing points and structural breaks of the series.
Findings
The changing points in the mean and variance but also the breaks in the structure justify the course of the closing price. From the information extracted in the analysis, it can be concluded that market sentiment is currently pessimistic due to the downward trend in the price. Both the public and the shareholders are disappointed with the performance of PATH stock and are waiting for the next change point that will change the trend of the series.
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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 and…
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
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Abstract
Purpose
The purpose of this paper is to propose an integrated approach to modeling and measuring supply chain performance and stability using system dynamics (SD) and the autoregressive integrated moving average (ARIMA).
Design/methodology/approach
SD and ARIMA models were developed, respectively, for modeling and measuring supply chain performance and for further analyzing and projecting supply chain stability for long‐term management. A case study from a typical semiconductor equipment manufacturing company is used to illustrate and validate the proposed method.
Findings
Effectiveness and efficiency, with six corresponding indicators (product reliability, employee fulfillment, customer fulfillment, on‐time delivery, profit growth, and working efficiency), were found to be the most significant factors in the performance of the supply chain. The results of the combined model provide evidence that supply chain performance of the case company is up to standard (average OPIN=0.64) and is considered stable, but still far from outstanding. Continuous improvement, especially in supply chain efficiency, is suggested in order to maximize performance.
Originality/value
This integrated approach is innovative and creates a new way for other disciplines. This study provides a practical and easy‐to‐use model that enables senior and top management decision makers and operation managers involved in the supply chain to assess, forecast, and take anticipatory action so that the supply chain can experience improvement in a timesaving and effective manner and achieve excellence in performance.
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