Predicting chaotic time-series with wavelet networks

A new technique, wavelet network, is introduced to predict chaotic time series. By using this technique, firstly, we make accurate short-term predictions of the time series from chaotic attractors. Secondly, we make accurate predictions of the values and bifurcation structures of the time series from dynamical systems whose parameter values are changing with time. Finally we predict chaotic attractors by making long-term predictions based on remarkably few data points, where the correlation dimensions of predicted attractors are calculated and are found to be almost identical to those of actual attractors.

Direct dynamical test for deterministic chaos and optimal embedding of a chaotic time-series

We propose here a local exponential divergence plot which is capable of providing an alternative means of characterizing a complex time series. The suggested plot defines a time-dependent exponent and a ''plus'' exponent. Based on their changes with the embedding dimension and delay time, a criterion for estimating simultaneously the minimal acceptable embedding dimension, the proper delay time, and the largest Lyapunov exponent has been obtained. When redefining the time-dependent exponent LAMBDA(k) curves on a series of shells, we have found that whether a linear envelope to the LAMBDA(k) curves exists can serve as a direct dynamical method of distinguishing chaos from noise.

Local exponential divergence plot and optimal embedding of a chaotic time-series

we propose here a local exponential divergence plot which is capable of providing a new means of characterizing chaotic time series. The suggested plot defines a time dependent exponent LAMBDA and a ''plus'' exponent LAMBDA+ which serves as a criterion for estimating simultaneously the minimal acceptable embedding dimension, the proper delay time and the largest Lyapunov exponent.

Reconstructing an 83-Year Time Series of Daily Sea Surface Temperature at Pacific Grove, California

Daily sea surface temperatures have been acquired at the Hopkins Marine Station in Pacific Grove, California since January 20, 1919.This time series is one of the longest oceanographic records along the U.S. west coast. Because of its length it is well-suited for studying climate-related and oceanic variability on interannual, decadal, and interdecadal time scales. The record, however, is not homogeneous, has numerous gaps, contains possible outliers, and the observations were not always collected at the same time each day. Because of these problems we have undertaken the task of reconstructing this long and unique series. We describe the steps that were taken and the methods that were used in this reconstruction. Although the methods employed are basic, we believe that they are consistent with the quality of the data. The reconstructed record has values at every time point, original, or estimated, and has been adjusted for time-of-day variations where this information was available. Possible outliers have also been examined and replaced where their credibility could not be established. Many of the studies that have employed the Hopkins time series have not discussed the issue of data quality and how these problems were addressed. Because of growing interest in this record, it is important that a single, well-documented version be adopted, so that the results of future analyses can be directly compared. Although additional work may be done to further improve the quality of this record, it is now available via the internet. [PDF contains 48 pages]