Recurrent neural networks for time series analysis: from AR to DeepAR
Bruni, Sofia (A.A. 2024/2025) Recurrent neural networks for time series analysis: from AR to DeepAR. Tesi di Laurea in Data analysis for business, Luiss Guido Carli, relatore Alessia Caponera, pp. 37. [Bachelor's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Time series analysis. Definition of time series model. Fundamental concepts. Stationary time series models. Models for nonstationary time series. Neural networks. Different types of neural networks. RNN: Recurrent neural network. When time series and RNN meets: DeepAR. RNN forecaster. RNN and AR compared. DeepAR. Code exploration and comparative forecasting using simpler statistical models. Practical example of modeling for client MT 022.
References
Bibliografia: p. 37.
| Thesis Type: | Bachelor's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18) |
| Chair: | Data analysis for business |
| Thesis Supervisor: | Caponera, Alessia |
| Academic Year: | 2024/2025 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 18 Dec 2025 15:43 |
| Last Modified: | 18 Dec 2025 15:43 |
| URI: | https://tesi.luiss.it/id/eprint/44525 |
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