Sek Jun Weon

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 7, No. 12, pp. 3866-3873, Dec. 2000
10.3745/KIPSTE.2000.7.12.3866,   PDF Download:

Abstract

In this paper, we investigates the long-term memory and the Correct answer rate of the foreign exchange data (Yen/Dollar) that is one of economic time series. There are many cases where two kinds of fractal dimensions exist in time series generated from dynamical systems such as AR models that are typical models having a short-term memory. The sample interval separating from these two dimensions are denoted by kcrossover. Let the fractal dimension be D1 in k < kcrossover, and D2 in k > kcrossover from the statistics model. In usual, Statistic models have dimensions D1 and D2 such that D1 < D2 and D2 2. But it showed a result contrary to this in the real time series such as NIKKEI. The exchange data that is one of real time series have relation of D1 > D2. When the interval between data increases, the correlation between data increases, which is quite a peculiar phenomenon. We predict exchange data by neural networks. We confirm that β obtained from prediction errors and D calculated from time series data precisely satisfy the relationship = 2-2D which is provided from a non-linear model having fractal dimension. And We identified that the difference of fractal dimension appeared in the Correct answer rate.

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