Prediction of Interbank Offered Rate Based on Time Series Model

Ding Huang; Ming Zhong; Xupeng Shi

doi:10.1051/e3sconf/202125101014

All issues

Volume 251 (2021)

E3S Web Conf., 251 (2021) 01014

References

Open Access

Issue		E3S Web Conf. Volume 251, 2021 2021 International Conference on Tourism, Economy and Environmental Sustainability (TEES 2021)


Article Number		01014
Number of page(s)		4
Section		Analysis of Energy Industry Economy and Consumption Structure Model
DOI		https://doi.org/10.1051/e3sconf/202125101014
Published online		15 April 2021

Esling, P., & Agon, C., (2012). Time-series data mining. Acm Computing Surveys, 45(1), 1–34. [Google Scholar]
Fu, T. C., (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1), 164–181. [Google Scholar]
Modarres, R., (2007). Streamflow drought time series forecasting. Stochastic Environmental Research & Risk Assessment, 21(3), 223–233. [Google Scholar]
Junior, P. R., Pamplona, E. D. O., & Fernando Luiz Riêra Salomon. (2014). Arima: an applied time series forecasting model for the bovespa stock index. Journal of Computer & Communications, 5(21), 3383–3391. [Google Scholar]
Thompson, O. Y., (2003). Short-term forecasting of crime. International Journal of Forecasting. [Google Scholar]
Nepal, B., Yamaha, M., Yokoe, A., & Yamaji, T. (2020). Electricity load forecasting using clustering and ARIMA model for energy management in buildings. Japan Architectural Review, 3(1), 62–76. [Google Scholar]
Dash, P. K., Ramakrishna, G., Liew, A. C., & Rahman, S. (1995). Fuzzy neural networks for time-series forecasting of electric load. IEE proceedingsgeneration, transmission and distribution, 142(5), 535–544. [Google Scholar]
Liu, X., Liu, H., Guo, Q., & Zhang, C. (2019). Adaptive wavelet transform model for time series data prediction. Soft Computing, 1–8. [Google Scholar]
Alves, L. G., Ribeiro, H. V., & Rodrigues, F. A. (2018). Crime prediction through urban metrics and statistical learning. Physica A: Statistical Mechanics and its Applications, 505, 435–443. [Google Scholar]
Taylor, Sean J., Letham, & Benjamin. (2018). Forecasting at scale. American Statistician, 72(1), 37–45. [Google Scholar]
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., & Zhang, C., (2020). Connecting the dots: multivariate time series forecasting with graph neural networks. [Google Scholar]

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[1] Esling, P., & Agon, C., (2012). Time-series data mining. Acm Computing Surveys, 45(1), 1–34. [Google Scholar]

[2] Fu, T. C., (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1), 164–181. [Google Scholar]

[3] Modarres, R., (2007). Streamflow drought time series forecasting. Stochastic Environmental Research & Risk Assessment, 21(3), 223–233. [Google Scholar]

[4] Junior, P. R., Pamplona, E. D. O., & Fernando Luiz Riêra Salomon. (2014). Arima: an applied time series forecasting model for the bovespa stock index. Journal of Computer & Communications, 5(21), 3383–3391. [Google Scholar]

[5] Thompson, O. Y., (2003). Short-term forecasting of crime. International Journal of Forecasting. [Google Scholar]

[6] Nepal, B., Yamaha, M., Yokoe, A., & Yamaji, T. (2020). Electricity load forecasting using clustering and ARIMA model for energy management in buildings. Japan Architectural Review, 3(1), 62–76. [Google Scholar]

[7] Dash, P. K., Ramakrishna, G., Liew, A. C., & Rahman, S. (1995). Fuzzy neural networks for time-series forecasting of electric load. IEE proceedingsgeneration, transmission and distribution, 142(5), 535–544. [Google Scholar]

[8] Liu, X., Liu, H., Guo, Q., & Zhang, C. (2019). Adaptive wavelet transform model for time series data prediction. Soft Computing, 1–8. [Google Scholar]

[9] Alves, L. G., Ribeiro, H. V., & Rodrigues, F. A. (2018). Crime prediction through urban metrics and statistical learning. Physica A: Statistical Mechanics and its Applications, 505, 435–443. [Google Scholar]

[10] Taylor, Sean J., Letham, & Benjamin. (2018). Forecasting at scale. American Statistician, 72(1), 37–45. [Google Scholar]

[11] Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., & Zhang, C., (2020). Connecting the dots: multivariate time series forecasting with graph neural networks. [Google Scholar]