E3S Web Conf.
Volume 251, 20212021 International Conference on Tourism, Economy and Environmental Sustainability (TEES 2021)
|Number of page(s)||4|
|Section||Analysis of Energy Industry Economy and Consumption Structure Model|
|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]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.