2025

• Dowe, D., Peiris, M., Kim, E. (2025). A Novel ARFIMA-ANN Hybrid Model for Forecasting Time Series - and its Role in Explainable AI. Journal of Econometrics and Statistics, 5(1), 107-127.
• Gadhi, A., Peiris, M., Allen, D., Hunt, R. (2025). Optimal Time Series Forecasting Through the GARMA Model. Econometrics, 13(1), 3-1-3-23. [More Information]

2024

• Allen, D., Mushunje, L., Peiris, M. (2024). GANs and synthetic financial data: calculating VaR*. Applied Economics. [More Information]
• Hunt, R., Peiris, M., Weber, N. (2024). Seasonal generalized AR models. Communications in Statistics - Theory and Methods, 53(3), 1065-1080. [More Information]

2023

• Hunt, R., Peiris, M., Weber, N. (2023). Bayesian estimation of Gegenbauer processes. Journal of Statistical Computation and Simulation, 93(9), 1357-1377. [More Information]
• Leong, X., Jajo, N., Peiris, M., Khadra, M. (2023). Forecasting Elective Surgery Demand Using ARIMA-Machine Learning Hybrid Model. European Journal of Artificial Intelligence and Machine Learning, 2(3), 1-7. [More Information]
• Allen, D., Mushunje, L., Peiris, M. (2023). GANs through the looking glass: How real is the fake financial data created by Generative Adversarial Neural Nets? The 25th International Congress on Modelling and Simulation (MODSIM2023), Australia: Modelling and Simulation Society of Australia and New Zealand Inc (MSSANZ).
• Allen, D., Peiris, M. (2023). GARMA, HAR and Rules of Thumb for Modelling Realized Volatility. Risks, 11(10), Article no. 179-1-Article no. 179-15. [More Information]
• Jajo, N., Peiris, M. (2023). Python and R in Statistics and Data Science. UK: LAP Lambert Academic Publishing.
• Peiris, M., Hunt, R. (2023). Revisiting the Autocorrelation of Long Memory Time Series Models. Mathematics, 11(4), 817-8 pages. [More Information]

2022

• Alhuntushi, N., Jajo, N., Peiris, M., Khadra, M., Mallows, J. (2022). A New Look at Patient Waiting Time in an Australian Emergency Department using Simulation. International Journal of Statistics and Systems, 17(1), 1-18.
• Zhou, J., Ng, K., Ng, K., Peiris, M., Koh, Y. (2022). Asymmetric Control Limits for Weighted-Variance Mean Control Chart with Different Scale Estimators under Weibull Distributed Process. Mathematics, 10(22), Article 4380-1-Article 4380-15. [More Information]
• Hunt, R., Peiris, M., Weber, N. (2022). Estimation methods for stationary Gegenbauer processes. Statistical Papers, 63(6), 1707-1741. [More Information]

2021

• Hunt, R., Peiris, S., Weber, N. (2021). A General Frequency Domain Estimation Method for Gegenbauer Processes. Journal of Time Series Econometrics, 13(2), 119-144. [More Information]
• Peiris, M., Chan, J., Jajo, N. (2021). A Quick Reference Guide to Beginners of Statistics and Data Science Using RStudio. Indonesia: CV. Meugah Printindo.
• Jajo, N., Peiris, M. (2021). A Study on Efficient Modelling in Higher Education Academic Workforce Using Simulation. European Journal of Mathematics and Statistics, 2(6), 7-14. [More Information]
• Rosadi, D., Arisanty, D., Andriyani, W., Peiris, M., Agustina, D., Dowe, D., Fang, Z. (2021). Improving Machine Learning Prediction of Peatlands Fire Occurrence for Unbalanced Data Using SMOTE Approach. 2021 International Conference on Data Science, Artificial Intelligence, and Business Analytics, DATABIA 2021, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers Inc. [More Information]
• Fang, Z., Dowe, D., Peiris, M., Rosadi, D. (2021). Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting. Entropy, 23 (Open Access)(12), 1601-1-1601-21. [More Information]
• Jajo, N., Peiris, M. (2021). Statistical analysis of ERA and the quality of research in Australian Universities. Journal of Applied Research in Higher Education, 13(2), 420-429. [More Information]

2020

• Asai, M., Peiris, M., McAleer, M., Allen, D. (2020). Cointegrated Dynamics for a Generalized Long Memory Process: Application to Interest Rates. Journal of Time Series Econometrics, 12(1), Article 20180024 - 1-Article 20180024 - 18. [More Information]
• Leong, X., Jajo, N., Peiris, S. (2020). Discrete Simulation on Elective Surgery Wait Line Using Arena Simulation Software. International Journal of Modeling and Optimization, 10(2), 47-51. [More Information]
• Phillip, A., Chan, J., Peiris, M. (2020). On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin. Econometrics and Statistics, 16, 69-90. [More Information]
• Asai, M., McAleer, M., Peiris, M. (2020). Realized stochastic volatility models with generalized Gegenbauer long memory. Econometrics and Statistics, 16, 42-54. [More Information]
• Peiris, M., Swartz, T. (2020). Revisiting the Kurtosis of Stationary Processes with Applications to Volatility Models. Journal of Statistical and Econometric Methods, 9(20 April 2019), 1-17.

2019

• Allen, D., Kalev, P., Peiris, M., Singh, A. (2019). Currency Spillover Effects between the US Dollar and Some Major Currencies and Exchange Rate Forecasts Based on Neural Nets. In Sabri Boubaker, Duc Khuong Nguyen (Eds.), Handbook of Global Financial Markets; Transformations, Dependence, and Risk Spillovers, (pp. 199-220). Singapore: World Scientific Publishing. [More Information]
• Peiris, S., Swartz, T. (2019). Developments and Applications of Biostatistical Time Series: A Review. Annals of Biostatistics & Biometric Applications, 3(5), 1-4. [More Information]
• Chan, J., Ng, K., Nitithumbundit, T., Peiris, M. (2019). Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models. Studies in Nonlinear Dynamics and Econometrics, 23(2), 1-22. [More Information]
• Phillip, A., Chan, J., Peiris, M. (2019). On long memory effects in the volatility measure of Cryptocurrencies. Finance Research Letters, 28, 95-100. [More Information]

2018

• Phillip, A., Chan, J., Peiris, M. (2018). A new look at Cryptocurrencies. Economics Letters, 163, 6-9. [More Information]
• Wu, H., Peiris, M. (2018). An introduction to vector Gegenbauer processes with long memory. Stat, 7(1), 1-20. [More Information]
• Phillip, A., Chan, J., Peiris, M. (2018). Bayesian estimation of Gegenbauer long memory processes with stochastic volatility: methods and applications. Studies in Nonlinear Dynamics and Econometrics, 22(3), 1-29. [More Information]
• Dissanayake, G., Peiris, M., Proietti, T. (2018). Fractionally Differenced Gegenbauer Processes with Long Memory: A Review. Statistical Science, 33(3), 413-426. [More Information]
• Yatigammana, R., Peiris, M., Gerlach, R., Allen, D. (2018). Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants. Risks, 6(52), 1-22. [More Information]

2017

• Ng, K., Peiris, M., Chan, J., Allen, D., Ng, K. (2017). Efficient modelling and forecasting with range based volatility models and its application. North American Journal of Economics and Finance, 42, 448-460. [More Information]
• Peiris, M., Asai, M., McAleer, M. (2017). Estimating and Forecasting with Generalized Fractional Long Memory Stochastic Volatility Models. Journal of Risk and Financial Management, 10(4), 1-16. [More Information]

2016

• Gerlach, R., Peiris, M., Lin, E. (2016). Bayesian estimation and inference for log-ACD models. Computational Statistics, 31(1), 25-48. [More Information]
• Peiris, M., Asai, M. (2016). Generalized Fractional Processes with Long Memory and Time Dependent Volatility Revisited. Econometrics, 4(3), 1-21. [More Information]
• Allen, D., McAleer, M., Peiris, M., Singh, A. (2016). Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies. Risks, 4(7), 1-14. [More Information]
• Dissanayake, G., Peiris, M., Proietti, T. (2016). State space modeling of Gegenbauer processes with long memory. Computational Statistics and Data Analysis, 100, 115-130. [More Information]

2015

• Ong, S., Biswas, A., Peiris, M., Low, Y. (2015). Count Distribution for Generalized Weibull Duration with Applications. Communications in Statistics - Theory and Methods, 44(19), 4203-4216. [More Information]
• Ng, K., Peiris, M., Thavaneswaran, A., Ng, K. (2015). Modelling the risk or price durations in financial markets: quadratic estimating functions and applications. Economic Computation and Economic Cybernetics Studies and Research, 49(1), 223-237.

2014

• Ng, K., Peiris, M., Gerlach, R. (2014). Estimation and forecasting with logarithmic autoregressive conditional duration models: A comparative study with an application. Expert Systems with Applications, 41(7), 3323-3332. [More Information]
• Dissanayake, G., Peiris, M., Proietti, T. (2014). Estimation of Generalized Fractionally Differenced Processes with Conditionally Heteroskedastic Errors. ITISE 2014 International Work Conference on Time Series Analysis, Granada: Copicentro Granada S L.
• Rosadi, D., Peiris, M. (2014). Second-order least-squares estimation for regression models with autocorrelated errors. Computational Statistics, 29(5), 931-943. [More Information]
• Peiris, M. (2014). Testing the null hypothesis of zero serial correlation in short panel time series: a comparison of tail probabilities. Statistical Papers, 55(2), 513-523. [More Information]

2013

• Shitan, M., Peiris, M. (2013). Approximate Asymptotic Variance-Covariance Matrix for the Whittle Estimators of GAR(1) Parameters. Communications in Statistics - Theory and Methods, 42(5), 756-770. [More Information]
• Peiris, M. (2013). Efficient Estimation of Regression Models with Heteroscedastic Errors. Mathematical Scientist, 38, 124-128.
• Allen, D., Ng, K., Peiris, M. (2013). Estimating and simulating Weibull models of risk or price durations: An application to ACD models. North American Journal of Economics and Finance, 25, 214-225. [More Information]
• Rosner, B., Peiris, M., Chan, J., Marchev, D. (2013). MATH1015: Biostatistics. Sydney: Cengage Learning.
• Ng, K., Peiris, M. (2013). Modelling High Frequency Transaction Data in Financial Economics: A Comparative Study Based on Simulations. Journal of Economic Computation and Economic Cybernetics Studies and Research, 47(2), 189-201.
• Allen, D., Ng, K., Peiris, M. (2013). The efficient modelling of high frequency transaction data: A new application of estimating functions in financial economics. Economics Letters, 120, 117-122. [More Information]

2012

• Pillai, T., Shitan, M., Peiris, M. (2012). Some Properties of the Generalized Autoregressive Moving Average (GARMA (1, 1; 1, 2)) Model. Communications in Statistics - Theory and Methods, 41(4), 699-716. [More Information]

2011

• Abdullah, N., Mohammed, I., Peiris, M., Azizan, A. (2011). A New Iterative Procedure for Estimation of RCA Parameters Based on Estimating Functions. Applied Mathematical Sciences, 5(4), 193-202.
• Peiris, M., Thavaneswaran, A., Appadoo, S. (2011). Doubly stochastic models with GARCH innovations. Applied Mathematics Letters, 24(11), 1768-1773. [More Information]
• Ng, K., Peiris, M., Lai, S., Tiew, C. (2011). Efficient Estimation of Autoregressive Conditional Duration (ACD) Models using Estimating Functions (EF). The International Statistics Conference 2011: Statistical Concepts and Methods for the Modern World, Colombo, Sri Lanka: Institute of Applied Statistics, Sri Lanka.
• Dissanayake, G., Peiris, M. (2011). Generalized Fractional Processes with Conditional Heteroscedasticity. Sri Lankan Journal of Applied Statistics, 12(Special Issue 2011), 1-12.
• Shitan, M., Peiris, M. (2011). Time Series Properties of the Class of Generalized First-Order Autoregressive Processes with Moving Average Errors. Communications in Statistics - Theory and Methods, 40(13), 2259-2275. [More Information]

2009

• Shitan, M., Peiris, M. (2009). A Note on the properties of generalised separable spatial autoregressive process. Journal of Probability and Statistics, 2009, 847830-1-847830-11. [More Information]
• Allen, D., Lazarov, Z., McAleer, M., Peiris, M. (2009). Comparison of Alternative ACD Models via density and interval forecasts: Evidence from the Australian Stock Market. Mathematics and Computers in Simulation, 79(8), 2535-2555. [More Information]
• Pathmanathan, N., Ng, K., Peiris, M. (2009). On Estimation of Autoregressive Conditional Duration (ACD) Models Based on Different Error Distributions. Sri Lankan Journal of Applied Statistics, 10, 251-269.
• Shitan, M., Peiris, M. (2009). On properties of the second order generalized autoregressive GAR(2) model with index. Mathematics and Computers in Simulation, 80(2), 367-377. [More Information]
• Pillai, T., Shitan, M., Peiris, M. (2009). Time series properties of the class of first order autoregressive processes with generalized moving average errors. Journal of Statistics: Advances in theory and applications, 2(1), 71-92.

2008

• Thavaneswaran, A., Peiris, M., Singh, J. (2008). Derivation of Kurtosis and option price formulae for popular volatility models with applications in finance. Communications in Statistics - Theory and Methods, 37(11), 1799-1814.
• Allen, D., Chan, F., McAleer, M., Peiris, M. (2008). Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks. Journal of Econometrics, 147(1), 163-185. [More Information]
• Shitan, M., Peiris, M. (2008). Generalized autoregressive (GAR) model: A comparison of maximum likelihood and whittle estimation procedures using a simulation study. Communications in Statistics: Simulation and Computation, 37(3), 560-570. [More Information]
• Thavaneswaran, A., Peiris, M., Appadoo, S. (2008). Random coefficient volatility models. Statistics and Probability Letters, 78, 582-593. [More Information]
• Perera, D., Peiris, M., Robinson, J., Weber, N. (2008). The empirical saddlepoint method applied to testing for serial correlation in panel time series data. Statistics and Probability Letters, 78(17), 2876-2882. [More Information]

2007

• Peiris, M., Ng, K., Mohamed, I. (2007). A review of recent developments of financial time series: ACD modelling using the estimating function approach. Sri Lankan Journal of Applied Statistics, 8(1), 1-17.
• Bertram, W., Peiris, M. (2007). An example of a classification problem applied to Australian equity data. Computational Statistics and Data Analysis, 51(8), 3627-3630. [More Information]
• Peiris, M., Thavaneswaran, A. (2007). An introduction to volatility models with indices. Applied Mathematics Letters, 20(2), 177-182. [More Information]

2006

• Perera, D., Peiris, M., Robinson, J., Weber, N. (2006). Saddlepoint approximation methods for testing of serial correlation in panel time series data. Journal of Statistical Computation and Simulation, 76(11), 1001-1015. [More Information]

2005

• Allen, D., Peiris, M., Yang, J. (2005). An examination of the role of time and its impact on price revision. Australian Journal of Management, 30(2), 283-301.
• Thavaneswaran, A., Appadoo, S., Peiris, M. (2005). Forecasting volatility. Statistics and Probability Letters, 75(1), 1-10. [More Information]
• Peiris, M. (2005). Generalised Autoregressive Models with Conditional Heteroscedasticity: An Application to Financial Time Series Modelling. The 2004 Workshop on Research Methods: Statistics and Finance, Wollongong: University of Wollongong.
• Bertram, W., Peiris, M. (2005). Increasing the Quality of Volatility Forecasts with Fractional ARIMA Models. The 2004 Workshop on Research Methods: Statistics and Finance, Wollongong: University of Wollongong.
• Peiris, M., Allen, D., Yang, W. (2005). Some statistical models for durations and an application to News Corporation stock prices. Mathematics and Computers in Simulation, 68(05-Jun), 549-556. [More Information]

2004

• Peiris, M., Rao, C. (2004). A Note On Testing For Serial Correlation In Large Number Of Small Samples Using Tail Probability Approximations. Communications in Statistics - Theory and Methods, 33(8), 1767-1777. [More Information]
• Peiris, M., Thavaneswaran, A. (2004). A Note On The Filtering For Some Time Series Models. Journal of Time Series Analysis, 25(3), 397-407. [More Information]
• Peiris, M., Rao, C. (2004). An Application Of Edgeworth Expansion On Testing For Serial Correlation In Large Number Of Small Samples. The International Sri Lankan Statistical Conference: Visions of Futuristic Methodologies, Australia: RMIT.
• Peiris, M., Allen, D., Thavaneswaran, A. (2004). An Introduction to Generalized Moving Average Models and Applications. Journal of Applied Statistical Science, 13(3), 251-267.
• Peiris, M., Peseta, T. (2004). Learning Statistics In First Year By Active Participating Students. Scholarly Inquiry into Science Teaching and Learning Symposium, Sydney, NSW: Uniserve Science.
• Perera, D., Peiris, M. (2004). Significance Testing For Lag One Serial Correlation In Repeated Measurements Using Saddlepoint Approximation. The International Sri Lankan Statistical Conference: Visions of Futuristic Methodologies, Australia: RMIT.
• Thavaneswaran, A., Peiris, M. (2004). Smoothed Estimates For Models With Random Coefficients And Infinite Variance Innovations. Mathematical and Computer Modelling, 39(4-5), 363-372. [More Information]

2003

• Ainkaran, P., Peiris, M., Mellor, R. (2003). A note on the analysis of short AR(1) type time series models with replicated observations. Workshop on Advanced Research Methods, Australia: University of Western Sydney.
• Perera, D., Peiris, M., Weber, N. (2003). A note on the distribution of serial correlation in large numbers of small samples. Advanced workshop on research methods, : National University of Singapore.
• Thavaneswaran, A., Peiris, M. (2003). Generalized smoothed estimating functions for nonlinear time series. Statistics and Probability Letters, 65(1), 51-56. [More Information]
• Peiris, M., Mellor, R., Ainkaran, P. (2003). Maximum likelihood estimation for short time series with replicated observations: a simulation study. InterStat, 11, 38719-42401.
• Hunt, R., Peiris, M., Weber, N. (2003). The bias of lag window estimators of the fractional difference parameter. Journal of Applied Mathematics and Computing, 12(1-2), 67-79. [More Information]

2002

• Peiris, M., Singh, N., Yadavalli, V. (2002). A note on the modelling and analysis of vector Arma processes with nonstationary innovations. Mathematical and Computer Modelling, 36(11-13), 1409-1424. [More Information]

2001

• Peiris, M., Thavaneswaran, A. (2001). Inference for some time series models with random coefficients and infinite variance innovations. Mathematical and Computer Modelling, 33, 843-849. [More Information]
• Peiris, M., Thavaneswaran, A. (2001). Multivariate stable ARMA processes with time dependent coefficients. Metrika: international journal for theoretical and applied statistics, 54, 131-138.
• Peiris, M., Thavaneswaran, A. (2001). On the properties of some nonstationary arma processes with infinite variance. International Journal Of Modelling And Simulation, 21, No. 4, 301-304.
• Peiris, M., Thavaneswaran, A. (2001). Recursive estimation for regression with infinite variance fractional ARIMA noise. Mathematical and Computer Modelling, 34, 1133-1137. [More Information]