Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future-Reference-Cited by-同舟云学术

Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future

Published:2023 Issue: Volume: Page:49-75
ISSN:2662-3862
Container-title:Palgrave Advances in the Economics of Innovation and Technology
language:
Short-container-title:

Author:

Spiliotis Evangelos

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-35879-1_3

Reference77 articles.

1. Agarap, A. F. (2018). Deep learning using rectified linear units (relu).

2. Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D. C., Rangapuram, S., Salinas, D., Schulz, J., Stella, L., Türkmen, A. C., & Wang, Y. (2019). Gluonts: Probabilistic time series models in python.

3. Alolayan, O. S., Raymond, S. J., Montgomery, J. B., & Williams, J. R. (2022). Towards better shale gas production forecasting using transfer learning. Upstream Oil and Gas Technology, 9, 100072.

4. Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: A decomposition approach to forecasting. International Journal of Forecasting, 16(4), 521–530.

5. Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1), 60–74.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Leveraging Hybrid Deep Learning Models for Enhanced Multivariate Time Series Forecasting;Neural Processing Letters;2024-08-23

2. Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble;Mineral Economics;2024-05-13