Name
Title | ||
|---|---|---|
Recurrent dictionary learning for state-space models with an application in stock forecasting |
Abstract | ||
|---|---|---|
•We introduce a new tool, called recurrent dictionary learning, whose core idea is to rely on a linear state-space model whose state transition and observation matrices are sequentially inferred, jointly with the resolution of the inherent probabilistic filtering problem, using an expectation-minimization approach.•Our numerical results on financial time series prediction from stock market data, show that our proposed method excels over state-of-the-art stock analysis models.•The advantages of our method are (i) the available uncertainty quantification on the estimates, (ii) the ease for dealing with multivariate input and output vectors, (iii) the automatic nature of the joint estimation problem (model parameters and sequence of the states). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.03.111 | Neurocomputing |
Keywords | DocType | Volume |
Stock forecasting,Recurrent dictionary learning,Kalman filter,Expectation-minimization,Dynamical modeling,Uncertainty quantification | Journal | 450 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shalini Sharma | 1 | 1 | 1.75 |
| Víctor Elvira | 2 | 0 | 0.68 |
| Emilie Chouzenoux | 3 | 202 | 26.37 |
| A. Majumdar | 4 | 644 | 75.83 |