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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings.

Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches.

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.

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.

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.

Finally, the validation of the proposed formula is investigated through comparisons with the nonlinear time-history analysis results.

The proposed formula can be effectively to estimate the distribution of the hysteretic energy under a given ground motion.

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.

Because of the consequences of changes in inflation, which affect the behavior of economic agents, the banking system, and the size of investments, high inflation provokes investors to withdraw funds from long-term projects and invest them in the banking sector. This effect reduces potential economic growth. In turn, the low level of inflation, which persists for a sufficient time interval of several years, indicates the stability of the national economy and attracts external and internal investors, contributes to economic growth. Based on this, it can be assumed that the presence of this relationship will be observed when analyzing data from national economies. Therefore, we can distinguish several hypotheses: inflation has a certain relationship with the indicator of financial development (h1); Inflation and financial development have a nonlinear relationship (h2); monetary policy implemented in the state can have a positive impact on the indicators of financial development, which will affect the dynamics of economic growth (h3). For this purpose, a sample was made from five countries: Germany, United States, Canada, China, Japan, and also Russia was selected in addition to them. This group of countries describes different aspects of the financial sector, as well as the level of economic development, so it will allow you to test hypotheses on a sufficient number of examples. The set of macroeconomic indicators of these countries is sufficiently studied, so it was easily amenable to econometric analysis.

A retrospective study of the interdependence of financial development and energy investment can show us that the opinions of scientists have changed quite dynamically in previous centuries regarding the role of the financial sector in the economy. This is primarily due to technological innovations and economic transformations. At the same time, it is worth mentioning the main approaches and directions prevailing in economics over the past time. The founder of the theory that financial markets have a positive impact on the development of the economy is the English economist Walter Bagehot. In his work, published in 1873, the author analyzes the economic processes of that time and, as a result, gets a general description of the ongoing interactions between the financial and real sectors. His most important conclusion for us is that loan capital, according to the economist, contributes to the expansion of production.

The different methods were used to measure the impact of financial development on energy investment. This is largely due to a certain set of factors that have the influence of their relationship. First of all, based on the research results, several groups of factors are identified that determine the nonlinear interaction of the financial sector and energy investment. This is characterized by the fact that at different values of these indicators, financial development has an ambiguous impact on energy investment. There are several such groups in total. The first ones are usually institutional factors, indicators that characterize the legal system and the level of development of social institutions. In the absence of a sufficient level of legal framework, financial markets will be unstable, which will undermine the demand for financial sector services. Countries with a high level of development of legal institutions have a guarantee for economic entities in the stable development of economic relations.

The development of mathematics allows scientists from related fields to build certain scientific models and conduct research. This is especially true for econometric research involving the processing of large amounts of data. One of the main roles in this case is played by a set of regression analysis methods. Its essence is to determine the influence of some variables on others. Up to this point, the studied topic of the relationship between economic growth and financial development was largely characterized by the construction of regression models, comparing them with each other, and determining the most fair and justified ones. The methods of these studies were different, as well as the results. This is due to the rapid development of this scientific field.