Out-sample Forecasts Research Articles

Detecting turning points of stock markets in China and the United States

ABSTRACT Choosing the appropriate method to identify turning points is critical for investors and policymakers. We propose a novel hybrid model combining dual long memory with structural breaks in the mean to identify the turning points of the stock market in China and the United States. The results show that models accounting for long memory and structural break perform well in the turning point detection. Notably, the proposed hybrid model generates superior in-sample turning point matching and out-sample forecasts for SSECI over those obtained from the competing ARFIMA models. In contrast, DJIA is better explained by the dual memory model with structural breaks in volatility. Finally, our analysis extends to encompass FTSE, HSI, IBOVESPA, and SENSEX30. The results demonstrate the superior performance of the hybrid model in markets exhibiting fractal characteristics. Such heterogeneity indicates differences in investor risk preference across stock markets and offers elaborate international evidence to the adaptive market hypothesis.

A Comparison of Predictive Modelling Techniques for Reducing Price Volatality in the Malaysia Sector

Malaysia is one of the most important contributors in fisheries to the economy, as the country's GDP is rose to 12%. Hence, the fisheries price has always been an issue for Malaysian citizens. Recently, planning and decision-making in fisheries have been tough for the fisherman as they need to bear low prices despite the higher fishing cost. At the same time, the retailer receives higher fisheries prices. Thus, the fisheries prices have risen over time and are subjected to price fluctuation. This study aims to forecast the monthly price behaviour of many main varieties of fisheries that are commonly consumed by Malaysian using the method of ARIMA, MLFFN and hybrid ARIMA-MLFFN. The data used in this study is divided by two which in-sample data of 7 years from January 2010 to December 2018, while out-sample data of 2 years from January 2019 to December 2020 regards with the ex-vessel, wholesale and retail price of the fishery (Longtail tuna, Torpedo scad and Shortfin scad). Forecasts were made for the price of fisheries in the future for the next twelve months. According to the findings of this study, we concluded that all of three model: ARIMA, MLFFN and hybrid ARIMA-MLFFN are comparable models since 96% MAPE of out-sample forecast were less than 10%, which is highly accurate forecast.

Forecasting container throughput of major Asian ports using the Prophet and hybrid time series models

Forecasting container throughput is critical for improved port planning, operations, and investment strategies. Reliability of forecasting methods need to be ensured before utilizing their outcomes in decision making. This study compares forecasting performances of various time series methods, namely autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), Holt-Winter's Exponential Smoothing (HWES), and the Prophet model. Since forecast combinations can improve performance, simple and weighted combinations of ARIMA, SARIMA and HWES have been explored, too. Monthly container throughput data of port of Shanghai, Busan, and Nagoya are used. The Prophet model outperforms others in the in-sample forecasting, while combined models outperform others in the out-sample forecasting. Due to the observed differences between the in-sample and out-sample forecast accuracy measures, this study proposes a forecast performance metric consistency check approach for informed real-world applications of forecasting models in port management decision-making.

Challenges for volatility forecasts of US fossil energy spot markets during the COVID-19 crisis

The outbreak of the COVID-19 pandemic led to a slowdown in the world's energy trade and changes in the use of energy resources. Meanwhile, global conditions are complex and can affect fossil energy spot markets, including crude oil, gasoline, heating oil, and natural gas. In this paper, we conduct comparative research to explore the impact of global conditions on fossil energy spot markets during the COVID-19 crisis based on the GARCH-MIDAS framework. We employ a 2010–2022 sample, which we cut off to investigate the differences before and after COVID-19. In-sample estimation shows that all global indicators are significant for forecasting the volatilities of these fossil energy spot prices. Out-sample forecasts reveal that GEPU and GECON outperform GPR and WIP for forecasting these four markets during the pre-COVID-19 period. After the crisis broke out, these global indicators can provide different forecasting information. Hence, this paper can be helpful for decision-makers to formulate and adjust pertinent policies and investments in the case of extreme emergencies in the future.

Trend and future perspective of milk production in Karnataka

The present study was conducted to understand the present and future perspective of milk production in Karnataka and to develop a suitable model for prediction of milk production in the state. The present study is having advantage as the past trends of milk production in the state have been impressive with growth, contributing significantly to overall milk production in the country. The Auto-Regressive Integrated Moving Average(ARIMA) and Artificial Neural Network (ANN) models were applied to the data sets of different dairy units for modeling and forecasting milk production in the state. The milk production of different dairy units has indicated a significant and positive trend over the years (Mann Kendal and Sen’s estimator). The purpose of the study was based on the accuracy criteria to identify the best ARIMA and ANN models for forecasting. It was found that the ARIMA model predicted cows, goats, and total milk production with better R-Square, MAPE and percentage prediction error compared to ANN models. ANN model performed better in predicting the buffalo’s milk yield compared to the ARIMA model. The best-identified models were used for out-sample forecast.

Application of SARIMA Model and Simple Seasonal Exponential Smoothing on Diabetes Mellitus: A Case of Enugu State Teaching Hospital, Nigeria

This paper aims at obtaining better model between seasonal ARIMA and simple seasonal exponential smoothing that will be used for forecasting number of diabetes patients in a given hospital. Monthly dataset from January 2009 to December 2019 from Enugu State Teaching Hospital was used for this research. Seasonal ARIMA was modelled using the techniques of Box-Jenkins, and simple seasonal exponential smoothing modelled using the least squares method. Bayesian Information Criterion (BIC) was employed to obtain the best seasonal ARIMA model, while the Theil’s U statistics and MAPE were used to obtain the best forecast model. ARIMA (1,1,2)(0,0,0)12 was selected as the best SARIMA model with the BIC of 7.873, and simple seasonal exponential smoothing was considered the best forecast model with a Theil’s U Statistic of 0.11241 and MAPE of 23.450. The fitted model was used to make out-sample forecast for the period January 2020-December 2025. The fitted model in this findings will help Enugu state government to plan efficiently, expand public sensitization, and allocate adequate resources for emergencies.

Application of Autoregressive Integrated Moving Average Model and Weighted Markov Chains on Forecasting Under-Five Mortality Rates in Nigeria

The aim of this paper is to obtain the best model that will be used to predict Under-Five Mortality Rate (U5MR) between Autoregressive Integrated Moving Average (ARIMA) model and Weighted Markov Chains (WMC). The annual dataset of U5MR in Nigeria for the period 1980-2019 is obtained from the official website of World Bank. The descriptive statistics and the unit root test for the stationarity of data were carried on the data series. ARIMA was modelled to U5MR using the techniques of Box-Jenkins while WMC was modelled using the techniques of k-means cluster analysis, Chi-Square, and Correlation. The best ARIMA model was obtained using Bayesian Information Criterion (BIC) while the best forecast model was obtained using Theil’s U Statistics and Mean Absolute Percentage Error (MAPE). U5MR attained stationarity after third differencing under ARIMA model dynamics. ARIMA(0,3,2) is considered the best ARIMA model with BIC of -2.679, and was selected as the best forecast model with Theil’s U Statistic of 0.000014 and MAPE of 0.174336%. The fitted model was used to make out-sample forecast for the period 2020-2030, which showed a steady decline. The findings of this paper will help in establishment and implementation health policies.

Projection of Infant Mortality Rate in Malaysia using R

Projecting future infant mortality rate (IMR) is an important subject in ensuring the stability of health in one nation or a specific region in general. Secondary data of IMR from December 1950 until December 2020 from United NationsWorld Population Prospects were used to project the trend of IMR in Malaysia up to 2023. In this study, five different forecasting models were adopted including Mean model, Naïve model, Autoregressive Integrated Moving Average (ARIMA) model, Exponential State Space model and Neural Network model. The results were analyzed using R programing and RStudio. The out-sample forecasts of mortality rates were evaluated using six error measures namely, Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE). Consequently, the keen analysis was focused on the trend and projection of infant mortality rate in the future using the most accurate model. The results showed that the “win” model for this study is ARIMA (0,2,0) model. The model provided a consistent estimate of IMR in relation to a similar decreasing pattern as shown by the original data and hence a reliable projection of IMR. The three ahead forecast values showed that IMR is likely to keep on continuously decreasing in the future. This study could become a guideline for human resource management and health care allocation planning. A forecast of IMR can help the implementation of interventions to reduce the burden of infant mortality within the target range.

Forecasting Sugarcane Yield of India based on rough set combination approach

This study applied a novel rough set combination approach for forecasting sugarcane production in India. The paper uses autoregressive integrated moving average (ARIMA), double exponential smoothing (DES) and Grey model (GM) to generate the single forecasts. Also, the weight coefficient is evaluated by underlying the rough set approach to combine the single forecasts obtained from different models. To validate our proposed analysis, Sugarcane from 1950 to 2011 was used for the overall empirical analysis and generate out-sample forecasts from 2012 to 2021 for the comparative analysis. Also, ARIMA (2, 1, 1) model is found more appropriate for forecasting Sugarcane production.

FORECASTING ELECTRICITY SUPPLIED IN TURKEY USING HOLT-WINTERS’ MULTIPLICATIVE METHOD AND ARTIFICIAL NEURAL NETWORK (ANN) MODELS

Electricity is one of the most essential necessities in today’s world and has an important role for the development of societies and economics. The need for electricity is expanding continuously due to increasing population, urbanization and industrialization. Hence, the purpose of this study was to develop the best model for forecasting electricity supplied in Turkey by applying the multiplicative Holt-Winters method and multilayer feed-forward neural network model. The monthly electricity supplied in Turkey from January 2000 until December 2019 were obtained from monthly electricity statistics report presented by the International Energy Agency (EIA). The data were divided into two sets comprising in-sample data from January 2000 until December 2015 and out-sample data from January 2016 to December 2019. The multiplicative Holt-Winters was used since the electricity supplied in Turkey exhibit trend and seasonal gave the out-sample forecast of 3.6990. The best multilayer feed forward neural network (MFFNN) model with three input lag variable, one hidden node, one output node, sigmoid transfer function in hidden layer and linear transfer function in output layer gave the out-sample forecast of 2.1483. Hence it can be concluded that, the multilayer feed-forward neural network model is more accurate than multiplicative Holt-Winters method to forecast the electricity supplied in Turkey.

Forecasting Malaysian mortality rates using the Lee-Carter model with fitting period variants

This study aims to forecast Malaysian mortality rates by age and gender using the well-known Lee-Carter model. Data obtained from the Department of Statistics Malaysia which consists of central mortality rates by age and gender from year 1970 to 2018. Two different sets of fitting periods were determined based on the observations of changes in mortality index patterns over the years. The Set A consists of 24-year fitting period from 1970 to 1993 whereas Set B comprises 31-year fitting period from 1970 to 2000. The in-sample evaluation of the Lee-Carter model are performed using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) whereas the out-sample errors are calculated using the Mean Absolute Percent Errors (MAPE) and Root Mean Square Error (RMSE). Results show that the set A is better fitted into the Lee-Carter model than that of set B by having lower values of AIC and BIC, consequently produced more accurate out-sample forecast values for females. The Lee-Carter model is a reliable model for Malaysia data, however careful attention must be given when selecting the best fitting period.

Time Series SARIMA Modelling and Forecasting of Monthly Rainfall and Temperature in the South Asian Countries

This study attempted to examine the future behaviour of monthly average rainfall and temperature of South Asian countries by using the Seasonal Autoregressive Moving Average model. Mann–Kendall trend test with Sen's Slope Estimator, to find the trending behaviour of all data series. The study has also been attempted to compare the above methods with the help of actual data. The monthly average rainfall and temperature of South Asian countries except Afghanistan and Maldives viz. Bangladesh, Bhutan, India, Nepal, Pakistan, Sri Lanka data from January, 1961 to December, 2016 have been collected from World Bank Group, Climate change knowledge portal. For estimating the trending behaviour, a non-parametric model such as the Mann–Kendall test was used with Sen's slope estimation to determine the magnitude of the trend. Box–Jenkins methodology was also used to develop the model and estimate the forecasting behaviour of rainfall and temperature in South Asian countries. Forecasting is carried out for both monthly rainfall and the average temperature of all the countries using best fitted models based on the data series. The monthly data from January, 1961 to December, 2010 are considered for validation of the model can be regarded as in-sample forecast and the data from January, 2011 to December, 2016 are used as out-sample forecast. The forecasting values with 95% confidence limit from January, 2011 to December, 2021 using best-fitted models for both rainfall and temperature. We conclude that climate change occurs for both rainfall and temperature in South Asian countries from the study period. The selected model can be used for forecasting both rainfall and temperature of respective countries from January, 2011 to December, 2021. As the climatic data analysis is valuable to understand the variation of global climatic change, this study may help for future research work on rainfall and temperature data.

Point forecasts of mortality rates in Malaysia: a comparison of principal component methods

<span>The problem of forecasting mortality has been widely discussed and is being considered as an important issue among actuaries and demographers. Using age and sex-specific mortality data in Malaysia, we compare and evaluate the point forecast accuracy of six principal component methods. These six methods are the Lee-Carter (LC) method, the unadjusted LC method (LCnone), the Lee-Miller (LM) variant, the Booth-Mandonald-Smith variant (BMS) and the two versions of the Hyndman-Ullah (HU). The overall performance of all methods was evaluated using out-sample forecasts based on the six error measures of three different data spanning with three different step-ahead forecasts. The data set consists of annual age-specific death rates (ASDR) for 17 age groups for both genders for the years 1980 to 2011. Results show that when the fitted period is longer, the LM method exhibits a better fit in Malaysia male ASDR and the HU6 method attains a better fit in Malaysia female ASDR. When the fitted period becomes shorter, the BMS method outperforms for male and HU1 for female ASDR. However, for overall performance, the LM method consistently outperforms others for Malaysia male ASDR and the LM and HU methods are the best good of fit for Malaysia ASDR.</span>

Double Seasonal ARIMA for Forecasting Electricity Demand of Kuaro Main Gate in East Kalimantan

This study uses intraday electricity load demand data from Kuaro Main Gate data in East Kalimantan as the basis of an empirical comparison of Double Seasonal ARIMA models for prediction up to a day ahead. For the purpose of this study, a one-year hourly Kuaro Main Gate data load demand from 1 January 2018 to December 2018 measured in Megawatt (MW) is used. In multiple times of load demand data, in addition to intraday and intra week cycles, and intra year seasonal cycle is also apparent. We extend the Double Seasonal ARIMA methods in order to accommodate the Intra year seasonal cycle. The mean absolute percentage error (MAPE) is used as the measure of forecasting accuracy. A notable feature of the time series is the presence of both an intraweek and an intraday seasonal cycle. We also propose that a Double Seasonal ARIMA model with the one-step-ahead forecast as the most appropriate model for forecasting the two-seasonal cycles Kuaro Main Gate data load demand time series. We use the Statistical Analysis System package to analyze the data. Using the least-squares method to estimate the coefficients in a Double Seasonal ARIMA model, followed by model validation and model selection criteria, we propose the ARIMA (1,1,1)(0,1,1)24(0,1,1)168 within-sample MAPE of 0.000992 as the best model for this study. Comparing the forecasting performances by using k-step ahead forecasts and one-step-ahead forecasts, we found that the MAPE for the one-step ahead out-sample forecasts from any horizon ranging from one week lead time to one month one week lead time are all less than 5%. Therefore we propose that a double seasonal ARIMA model with a one-step-ahead forecast must be considered in forecasting time series data with two seasonal cycles.

Modeling time-varying coffee price volatility in Ethiopia

ABSTRACT Recently, modeling and forecasting of high-frequency data (such as daily price) volatility using GARCH-MIDAS attract the attention of many researchers. Thus, the objective of this study is to model the average daily coffee price volatility from 1 January 2010 to 30 June 2019. The GARCH-MIDAS component model decomposes the conditional variance into short run component which follows a mean-reverting unit GARCH process and long-run component which consider different frequency macroeconomic indicators via mixed interval data sampling (MIDAS) specification. Unit root test results show the return series are stationary at level, while macroeconomic variables are stationary at first difference except interest rate, which is stationary at level. From the result of estimated model, all selected indicators are crucial in explaining price volatility. . Moreover, the estimated GARCH-MIDAS model with money supply as a main driver is used for out-sample forecast. Based on, DM test statistic multiplicative GARCH-MIDAS model provides an explanation for stylized facts that cannot be captured by standard GARCH model.

MULTIVARIATE LONG-MEMORY COHORT MORTALITY MODELS

AbstractThe existence of long memory in mortality data improves the understandings of features of mortality data and provides a new approach for establishing mortality models. The findings of long-memory phenomena in mortality data motivate us to develop new mortality models by extending the Lee–Carter (LC) model to death counts and incorporating long-memory model structure. Furthermore, there are no identification issues arising in the proposed model class. Hence, the constraints which cause many computational issues in LC models are removed. The models are applied to analyse mortality death count data sets from three different countries divided according to genders. Bayesian inference with various selection criteria is applied to perform the model parameter estimation and mortality rate forecasting. Results show that multivariate long-memory mortality model with long-memory cohort effect model outperforms multivariate extended LC cohort model in both in-sample fitting and out-sample forecast. Increasing the accuracy of forecasting of mortality rates and improving the projection of life expectancy is an important consideration for insurance companies and governments since misleading predictions may result in insufficient funds for retirement and pension plans.

Selection of Linear Time Series Model on the Basis of Out-of-Sample Prediction Criteria

Background: In linear time series, the in-sample model selection and the out-of-sample model selection are the two common approaches to model selection. However, empirical evidence based on out-of-sample forecast performance is generally considered more trustworthy than evidence based on in-sample performance, which is deficient in providing information about future observations.
 Aim: The aim of the study is to select the best linear time series model suitable to predict Nigeria exchange rates for the period 2002-2018.
 Materials and Methods: Data on naira to pound and naira to euro exchange rates from January 2002 to December 2018, comprising of 204 data points were considered. The data were divided into two portions; the first portion which comprises of 192 observations was used for model building while the second portion with 12 observations was used for out-of-sample prediction evaluation. The Box-Jenkins ARIMA iterative procedure was used in model building while the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and Theil’s U coefficient were the measures of accuracy adopted in selecting the best out-of sample model.
 Results: Our results revealed that, based on in-sample model selection, ARIMA (0, 1, 1) and ARIMA (1, 1, 0) were the appropriate models with minimum information criteria. However, on the basis of out-sample forecast performance evaluation criteria, ARIMA (1, 1, 0) and ARIMA (1, 1, 2) were found to be appropriate out-of-sample models with minimum forecast evaluation criteria. In all, our results showed that, the out-of sample models performed better than their in-sample counterparts in their ability to forecast future values.
 Conclusion: So far, this study revealed that out-of-sample is a better model selection criterion than the in-sample counterpart as evident in its ability to predict future values which is the very essence for modelling in time series.

Modeling and Forecasting Periodic Time Series data with Fourier Autoregressive Model

Most frequently used models for modeling and forecasting periodic climatic time series do not have the capability of handling periodic variability that characterizes it. In this paper, the Fourier Autoregressive model with abilities to analyze periodic variability is implemented. From the results, FAR(1), FAR(2) and FAR(2) models were chosen based on Periodic Autocorrelation function (PeACF) and Periodic Partial Autocorrelation function (PePACF). The coefficients of the tentative model were estimated using a Discrete Fourier transform estimation method. FAR(1) models were chosen as the optimal model based on the smallest values of Periodic Akaike (PAIC) and Bayesian Information criteria (PBIC). The residual of the fitted models was diagnosed to be white noise. The in-sample forecast showed a close reflection of the original rainfall series while the out-sample forecast exhibited a continuous periodic forecast from January 2019 to December 2020 with relatively small values of Periodic Root Mean Square Error (PRMSE), Periodic Mean Absolute Error (PMAE) and Periodic Mean Absolute Percentage Error (PMAPE). The comparison of FAR(1) model forecast with AR(3), ARMA(2,1), ARIMA(2,1,1) and SARIMA( 1,1,1)(1,1,1)12 model forecast indicated that FAR(1) outperformed the other models as it exhibited a continuous periodic forecast. The continuous monthly periodic rainfall forecast indicated that there will be rapid climate change in Nigeria in the coming yearly and Nigerian Government needs to put in place plans to curtail its effects.

Modeling And Forecasting Ghana’s Inflation Rate Under Threshold Models

In this study, we model and forecast Ghana’s inflation rate using nonlinear models between the period January 1981 and August 2016. Nonlinearity tests were conducted on the logarithm of the monthly rates of inflation using Keenan and Tsay tests, and based on the results, we rejected the null hypothesis of linearity of monthly rates of inflation. We used threshold models and compared their fitness and forecasting performance with standard linear Autoregressive (AR) models. We found out that the Self-Exiting Threshold Autoregressive (SETAR) and Logistic Smooth Threshold Autoregressive (LSTAR) models fit the data better. The simple linear Autoregressive (AR) models however, out-performed the nonlinear models in terms of forecasting. Various research projects have been carried out in this area of inflation modeling in Ghana, but these researchers modeled inflation in Ghana using nonlinear models that did not account for the conditional heteroscedasticity in the model. These models have been used and empirical evidence of their relative performance has been given for the success of developed economies such as US and Europe. However, limited studies have been done in the case of Ghana. This indicates a gap in the literature and poses a challenge as to which of these models is the optimal choice for modeling economic and financial data such as inflation rates for developing countries. It is recommended that policy makers, industry players and all those interested in modeling future rates of inflation in Ghana should consider using the threshold models instead of the traditional Box and Jenkins models since the threshold models are able to capture the heteroscedasticity in the model. Also by using the threshold models, policy makers and industry players would be able to properly capture the variability persistence in the monthly rates of inflation and hence estimates would be more accurate. Lastly, from the upward trend of the out-sample forecasts, it can be predicted that Ghana would experience double digit inflation in 2017. This would have several impacts on many aspects of the economy and could erode the economic gains made in the year 2016.

Comparison of Stochastic Mortality Model for Wider Age Range

The incorporation of non-linear pattern of early ages has opened new research directions on improving the existing stochastic mortality model structure. Several authors have outlined the importance of encompassing the full age range in dealing with longevity risk exposure by not to ignore the dependence between young and old age. In this study, we consider the two extensions of Cairns, Blake and Dowd model that incorporate the irregularity profile seen at the mortality of lower ages which are Plat and O’Hare and Li. The models’ performances in terms of in-sample fitting and out-sample forecasts were examined and compared. The results indicated that O’Hare and Li model performs better as compared to Plat model