time series analysis for a 1/tβ memory function and comparison with the lyapunov exponent using volatility scaling
Clicks: 128
ID: 249453
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
128 views
17 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #17 of 24 articles by views in membranes
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Being able to provide accurate forecasts on the trending behaviour of time series is
important in a range of applications involving the real-time evolution of signals, most notably in financial time series analysis, but control engineering in general. This paper reports on the use of an indicator that is based on a Memory Function of the form $\sim 1/t^{\beta},\hskip 0.1truein \beta>0$, and, in terms of a comparative analysis, the Lyapunov Exponent $\lambda$ coupled with an approach whereby both parameters (i.e. $\lambda$ and $\beta-1$) are scaled according to the corresponding Volatility $\sigma$ of the time series. A \lq back-testing' procedure is used to evaluate and compare the performance of the indices $(\beta-1)/\sigma$ and $\lambda/\sigma$ for forecasting and quantifying trends over a range of time scales. However, in either case, a critical solution for providing high accuracy forecasts is the filtering operation used to identify the position in time at which a trend occurs subject to a time delay factor that is inherent in the filtering strategy used. The paper explores this strategy and presents some example results that provide a quantitative measure of the accuracy obtained.
| Reference Key |
walsh2016mathematicatime
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Paddy Walsh;Jonathan Blackledge |
| Journal | membranes |
| Year | 2016 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.