Name
Abstract | ||
|---|---|---|
In this paper, we investigate a financial data set from [5] using two algorithms which are both designed for visualising data. One algorithm consists of a neuroscale algorithm which uses different Bregman divergences. The other uses a similar algorithm but based on reservoir computing. We show that the latter is much better because it captures the dynamical nature of the financial time series and thus reveals more explicit information. By investigating a slightly different cost function, we show that the latter mapping is not creating information which does not exist in the original time series. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CEC.2012.6253005 | 2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) |
Keywords | Field | DocType |
time series,algorithm design and analysis,reservoirs,training data,time series analysis,reservoir computing,data visualization,cost function,data visualisation | Data mining,Time series,Computer science,Temporal database,Artificial intelligence,Financial data processing,Training set,Mathematical optimization,Data visualization,Algorithm design,Visualization,Reservoir computing,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.37 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tzai-der Wang | 1 | 119 | 15.65 |
| Xiaochuan Wu | 2 | 2 | 1.11 |
| Colin Fyfe | 3 | 508 | 55.62 |