R.M. Kapila Tharanga Rathnayaka and D.M.K.N. Seneviratna
The time series analysis is an essential methodology which comprises the tools for analyzing the time series data to identify the meaningful characteristics for making future…
Abstract
Purpose
The time series analysis is an essential methodology which comprises the tools for analyzing the time series data to identify the meaningful characteristics for making future ad-judgments. The purpose of this paper is to propose a Taylor series approximation and unbiased GM(1,1) based new hybrid statistical approach (HTS_UGM(1,1)) for forecasting time series data under the poor, incomplete and uncertain information systems in a short period of time manner.
Design/methodology/approach
The gray forecasting is a dynamical methodology which can be classified into different categories based on their respective functions. The new proposed methodology is made up of three different methodologies including the first-order unbiased GM(1,1), Markov chain and Taylor approximation. In addition to that, two different traditional gray operational mechanisms include GM(1,1) and unbiased GM(1,1) used as the comparisons. The main objective of this study is to forecast gold price demands in a short-term manner based on the data which were taken from the Central Bank of Sri Lanka from October 2017 to December 2017.
Findings
The error analysis results suggested that the new proposed HTS_UGM(1,1) is highly accurate (less than 10 percent) with lowest RMSE error values in a one head as well as weakly forecasting’s than separate gray forecasting methodologies.
Originality/value
The findings suggested that the new proposed hybrid approach is more suitable and effective way for forecasting time series indices than separate time series forecasting methodologies in a short-term manner.
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D.M.K.N. Seneviratna and R.M. Kapila Tharanga Rathnayaka
The Coronavirus (COVID-19) is one of the major pandemic diseases caused by a newly discovered virus that has been directly affecting the human respiratory system. Because of the…
Abstract
Purpose
The Coronavirus (COVID-19) is one of the major pandemic diseases caused by a newly discovered virus that has been directly affecting the human respiratory system. Because of the gradually increasing magnitude of the COVID-19 pandemic across the world, it has been sparking emergencies and critical issues in the healthcare systems around the world. However, predicting the exact amount of daily reported new COVID cases is the most serious issue faced by governments around the world today. So, the purpose of this current study is to propose a novel hybrid grey exponential smoothing model (HGESM) to predicting transmission dynamics of the COVID-19 outbreak properly.
Design/methodology/approach
As a result of the complications relates to the traditional time series approaches, the proposed HGESM model is well defined to handle exponential data patterns in multidisciplinary systems. The proposed methodology consists of two parts as double exponential smoothing and grey exponential smoothing modeling approach respectively. The empirical analysis of this study was carried out on the basis of the 3rd outbreak of Covid-19 cases in Sri Lanka, from 1st March 2021 to 15th June 2021. Out of the total 90 daily observations, the first 85% of daily confirmed cases were used during the training, and the remaining 15% of the sample.
Findings
The new proposed HGESM is highly accurate (less than 10%) with the lowest root mean square error values in one head forecasting. Moreover, mean absolute deviation accuracy testing results confirmed that the new proposed model has given more significant results than other time-series predictions with the limited samples.
Originality/value
The findings suggested that the new proposed HGESM is more suitable and effective for forecasting time series with the exponential trend in a short-term manner.
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R.M. Kapila Tharanga Rathnayaka and D.M.K.N. Seneviratna
The global population has been experiencing an unprecedentedly rapid demographic transition as the populations have been growing older in many countries during the current…
Abstract
Purpose
The global population has been experiencing an unprecedentedly rapid demographic transition as the populations have been growing older in many countries during the current decades. The purpose of this study is to introduce a Grey Exponential Smoothing model (GESM)-based mechanism for analyzing population aging.
Design/methodology/approach
To analyze the aging population of Sri Lanka, initially, three major indicators were considered, i.e. total population, aged population and proportion of the aged population to reflect the aging status of a country. Based on the latest development of computational intelligence with Grey techniques, this study aims to develop a new analytical model for the analysis of the challenge of disabled and frail older people in an aging society.
Findings
The results suggested that a well-defined exponential trend has been seen for the population ages 65 and above, a total of a million) during 1960–2022; especially, the aging population ages 65 and above has been rising rapidly since 2008. This will increase to 24.8% in 2040 and represents the third highest percentage of elderly citizens living in an Asian country. By 2041, one in every four Sri Lankans is expected to be elderly.
Originality/value
The study proposed a GESM-based mechanism for analyzing the population aging in Sri Lanka based on the data from 1960 to 2022 and forecast the aging demands in the next five years from 2024 to 2028.
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R.M. Kapila Tharanga Rathnayaka, D.M.K.N. Seneviratna, Wei Jianguo and Hasitha Indika Arumawadu
The time series forecasting is an essential methodology which can be used for analysing time series data in order to extract meaningful statistics based on the information…
Abstract
Purpose
The time series forecasting is an essential methodology which can be used for analysing time series data in order to extract meaningful statistics based on the information obtained from past and present. These modelling approaches are particularly complicated when the available resources are limited as well as anomalous. The purpose of this paper is to propose a new hybrid forecasting approach based on unbiased GM(1,1) and artificial neural network (UBGM_BPNN) to forecast time series patterns to predict future behaviours. The empirical investigation was conducted by using daily share prices in Colombo Stock Exchange, Sri Lanka.
Design/methodology/approach
The methodology of this study is running under three main phases as follows. In the first phase, traditional grey operational mechanisms, namely, GM(1,1), unbiased GM(1,1) and nonlinear grey Bernoulli model, are used. In the second phase, the new proposed hybrid approach, namely, UBGM_BPNN was implemented successfully for forecasting short-term predictions under high volatility. In the last stage, to pick out the most suitable model for forecasting with a limited number of observations, three model-accuracy standards were employed. They are mean absolute deviation, mean absolute percentage error and root-mean-square error.
Findings
The empirical results disclosed that the UNBG_BPNN model gives the minimum error accuracies in both training and testing stages. Furthermore, results indicated that UNBG_BPNN affords the best simulation result than other selected models.
Practical implications
The authors strongly believe that this study will provide significant contributions to domestic and international policy makers as well as government to open up a new direction to develop investments in the future.
Originality/value
The new proposed UBGM_BPNN hybrid forecasting methodology is better to handle incomplete, noisy, and uncertain data in both model building and ex post testing stages.
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Youyang Ren, Yuhong Wang, Lin Xia, Wei Liu and Ran Tao
Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch…
Abstract
Purpose
Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers.
Design/methodology/approach
This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor.
Findings
This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models.
Originality/value
The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.
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Mohamed Ismail Mohamed Riyath, Narayanage Jayantha Dewasiri, Mohamed Abdul Majeed Mohamed Siraju, Athambawa Jahfer and Kiran Sood
Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market…
Abstract
Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market.
Need for the Study: The external market’s internal/own shocks and volatility spillovers influence portfolio choices in domestic stock market returns. Hence, it is required to investigate the internal shock in the domestic market and the external volatility spillovers from other countries.
Methodology: This study employs a quantitative method using ARMA(1,1)-GARCH(1,1) model. All Share Price Index (ASPI) is the proxy for the Colombo Stock Exchange (CSE) stock return. It uses daily time-series data from 1st April 2010 to 21st June 2023.
Findings: The findings revealed that internal/own and external shocks substantially impact the stock price volatility in CSE. Significant volatility clusters and persistence with extended memory in ASPI confirm internal/own shock in the market. Furthermore, CSE receives significant volatility shock from the USA, confirming external shock. This study’s findings highlight the importance of considering internal and external shocks in portfolio decision-making.
Practical Implications: Understanding the influence of internal shocks helps investors manage their portfolios and adapt to market volatility. Recognising significant volatility spillovers from external markets, especially the USA, informs diversification strategies. From a policy standpoint, the study emphasises the need for robust regulations and risk management measures to address shocks in domestic and global markets. This study adds value to the literature by assessing the sources of volatility shocks in the CSE, employing the ARMA-GARCH, a sophisticated econometrics model, to capture stock returns volatility, enhancing understanding of the CSE’s volatility dynamics.
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R.M. Kapila Tharanga Rathnayaka, D.M.K.N Seneviratna and Wei Jianguo
Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with…
Abstract
Purpose
Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions.
Design/methodology/approach
High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose.
Findings
The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings.
Practical implications
Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches.
Originality/value
However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.
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The study of the character of structural hysteretic energy under earthquake is an essential foundation for energy-based seismic design and evaluation method. The purpose of this…
Abstract
Purpose
The study of the character of structural hysteretic energy under earthquake is an essential foundation for energy-based seismic design and evaluation method. The purpose of this paper is to explore the distribution law of the accumulative irrecoverable hysteretic energy for MDOF structures, a formula of the accumulated irrecoverable hysteretic energy ratio along the layers is derived.
Design/methodology/approach
The procedure is based on the energy balance principle and the concept of the equivalent single-degree-of-freedom system. Furthermore, sensitivity analysis is carried out for 16 working conditions, considering all these possibilities of local failure or damage. And then the sensitivity influencing rule is obtained and the proposed formula is simplified.
Findings
Finally, the validation of the proposed formula is investigated through comparisons with the nonlinear time-history analysis results.
Originality/value
The proposed formula can be effectively to estimate the distribution of the hysteretic energy under a given ground motion.
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Yuhong Wang and Qi Si
This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.
Abstract
Purpose
This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.
Design/methodology/approach
In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.
Findings
The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.
Originality/value
The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.
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In modern conditions energy investment in Russia the world and cash flow status sectors is sufficient tense: financial institutions are forced to act in the following conditions…
Abstract
In modern conditions energy investment in Russia the world and cash flow status sectors is sufficient tense: financial institutions are forced to act in the following conditions: deficiencies in money supply and underestimated financial assets and liabilities of active site balance of payments. However, fast ones actions regulators contribute to mitigation measures the crisis. This very clearly shows the importance of monetary policy in Russia in regards to modern economic relations. Modern reality of development of energy investment and a new period of functioning of the economy were determined by necessity for revision of theoretical data basics and practical analysis of monetary policies, which determine the basic direction of this study. Monetary policy does not apply to financial institutions. It is fully autonomous, as end goals of monetary and credit control system regulations coincide with basic principle objectives of the macroeconomic policies of countries.