How to cite this paper
Setyanto, G., Jaya, I & Kristiani, F. (2024). Bayesian semi-shared temporal modeling: A comprehensive approach to forecasting multiple stock prices.International Journal of Data and Network Science, 8(3), 1947-1958.
Refrences
Blangiardo, M., & Cameletti, M. (2015). Spatial and Spatio-temporal Bayesian Models with R-INLA. Chennai: John Wiley & Sons.
Chatfield, C. (2001). Time-Series Forecasting. USA: Chapman & Hall/CRC.
Girard, A., Rasmussen, C., Candela, J., & Murray-S, R. (2002). Gaussian process priors with uncertain inputs application to multiple-step ahead time series forecasting. Advances in Neural Information Processing Systems, 15, 1-8.
Gomez-Rubio, V., Palmı-Perales, F., Lopez-Abente, G., Ramis-Prieto, R., & Fernandez-Navarro, P. (2019). Bayesian joint spatio-temporal analysis of multiple diseases. SORT, 43(1), 51-74.
Guberti, M. (2023, November 28). 3 Top-Rated E-Commerce Stocks That Analysts Are Loving Now. Retrieved January 14, 2024, from InvestorPlace: https://investorplace.com/2023/11/3-top-rated-e-commerce-stocks-that-analysts-are-loving-now/
Gupta, S. (2021, February 18). Amazon vs. MercadoLibre: Which E-Commerce Stock is a Better Buy? Retrieved January 14, 2024, from StockNews: https://stocknews.com/news/amzn-meli-amazon-vs-mercadolibre-which-e-commerce-stock-is-a-better-buy/
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computing, 8(9), 1735–1780.
Jaya, I., Chadidjah, A., Andriyana, Y., Setyanto, G., Supartini, E., & Kristiani, F. (2023a). Multiple endemic disease risk modeling using a Bayesian spatiotemporal shared components model. Decision Science Letters, 12(2), 389-398.
Jaya, I., Handoko, B., Andriyana, Y., Chadidjah, A., Kristiani, F., & Antikasari, M. (2023b). Multivariate Bayesian Semi-parametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia. Mathematics, 11(17), 3641.
Jaya, I., Folmer, H., & Lundberg, J. (2022). A joint Bayesian spatiotemporal risk prediction model of COVID-19 inci-dence, IC admission, and death with application to Sweden. The Annals of Regional Science, 1-45.
Knorr-Held, L., & Best, N. (2001). A Shared Component Model for Detecting Joint and Selective Clustering of Two Dis-eases. Journal of the Royal Statistical Society. Series A (Statistics in Society), 164(1), 73-85.
Law, J., & Abdullah, A. (2022). An Offenders‐Offenses Shared Component Spatial Model for Identifying Shared and Spe-cific Hotspots of Offenders and Offenses: A Case Study of Juvenile Delinquents and Violent Crimes in the Greater To-ronto Area. Journal of Quantitative Criminology, 1-24.
Reinsel, G. (1993). Element of Multivariate Time Series. New York: Springer-Verlag.
Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181-1191.
Sapankevych, N. I., & Sankar, R. (2009). Time series prediction using support vector machines: a survey. IEEE computa-tional intelligence magazine, 4(2), 24-38.
Wang, X., Liu, H., Du, J., & Dong, X. (2023). A long-term multivariate time series forecasting network combining series decomposition and convolutional neural networks. Applied Soft Computing, 139, 110214.
Chatfield, C. (2001). Time-Series Forecasting. USA: Chapman & Hall/CRC.
Girard, A., Rasmussen, C., Candela, J., & Murray-S, R. (2002). Gaussian process priors with uncertain inputs application to multiple-step ahead time series forecasting. Advances in Neural Information Processing Systems, 15, 1-8.
Gomez-Rubio, V., Palmı-Perales, F., Lopez-Abente, G., Ramis-Prieto, R., & Fernandez-Navarro, P. (2019). Bayesian joint spatio-temporal analysis of multiple diseases. SORT, 43(1), 51-74.
Guberti, M. (2023, November 28). 3 Top-Rated E-Commerce Stocks That Analysts Are Loving Now. Retrieved January 14, 2024, from InvestorPlace: https://investorplace.com/2023/11/3-top-rated-e-commerce-stocks-that-analysts-are-loving-now/
Gupta, S. (2021, February 18). Amazon vs. MercadoLibre: Which E-Commerce Stock is a Better Buy? Retrieved January 14, 2024, from StockNews: https://stocknews.com/news/amzn-meli-amazon-vs-mercadolibre-which-e-commerce-stock-is-a-better-buy/
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computing, 8(9), 1735–1780.
Jaya, I., Chadidjah, A., Andriyana, Y., Setyanto, G., Supartini, E., & Kristiani, F. (2023a). Multiple endemic disease risk modeling using a Bayesian spatiotemporal shared components model. Decision Science Letters, 12(2), 389-398.
Jaya, I., Handoko, B., Andriyana, Y., Chadidjah, A., Kristiani, F., & Antikasari, M. (2023b). Multivariate Bayesian Semi-parametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia. Mathematics, 11(17), 3641.
Jaya, I., Folmer, H., & Lundberg, J. (2022). A joint Bayesian spatiotemporal risk prediction model of COVID-19 inci-dence, IC admission, and death with application to Sweden. The Annals of Regional Science, 1-45.
Knorr-Held, L., & Best, N. (2001). A Shared Component Model for Detecting Joint and Selective Clustering of Two Dis-eases. Journal of the Royal Statistical Society. Series A (Statistics in Society), 164(1), 73-85.
Law, J., & Abdullah, A. (2022). An Offenders‐Offenses Shared Component Spatial Model for Identifying Shared and Spe-cific Hotspots of Offenders and Offenses: A Case Study of Juvenile Delinquents and Violent Crimes in the Greater To-ronto Area. Journal of Quantitative Criminology, 1-24.
Reinsel, G. (1993). Element of Multivariate Time Series. New York: Springer-Verlag.
Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181-1191.
Sapankevych, N. I., & Sankar, R. (2009). Time series prediction using support vector machines: a survey. IEEE computa-tional intelligence magazine, 4(2), 24-38.
Wang, X., Liu, H., Du, J., & Dong, X. (2023). A long-term multivariate time series forecasting network combining series decomposition and convolutional neural networks. Applied Soft Computing, 139, 110214.