# Independent component ordering in ICA time series analysis

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2 146 Y.-m. Cheung, L. Xu / Neurocomputing 41 (2001) 145}152 analysis (PCA) is a prevalent analysis tool, whose advantages are generally two-fold: The order of uncorrelated principal components is explicitly given in terms of their variances. The underlying structure of series can be revealed in using the "rst few principal components. However, the PCA technique only uses second-order statistics information, which makes the principal components de-correlated but not really independent. Recently, it has been realized that independent component analysis (ICA), rather than PCA, is more appropriate for time-series analysis. The reason is that the extraction of independent components by ICA involves higher-order statistics, which makes the components reveal more useful information than principal components. The paper [3] has shown the advantage of ICA in analyzing a Japanese stock price in comparison with PCA. In the community, ICA has been providing a new tool for time-series analysis as shown in [7,8]. In analyzing time series by ICA, we often need to select several dominant components from the component pool, which can bring in at least two advantages: 1. Robust Performance * Those dominant components reveal the major information about the stochastic mechanism that gives rise to the observed series. Without considering trivial details, the model built based on these components has a robust performance in series forecasting; 2. Reduction of Analysis Complexity * To well understand the mechanism in the background of the series, sometimes we need to interpret the physical meaning of the independent components, which is a non-trivial task. Hence, studies concentrating on those dominant components make the analysis more easy. One possible way to choose dominant components is by two separating steps as follows: Step 1. List all the independent components in an appropriate order; Step 2. Select the "rst few components in the order as dominant ones. An empirical study in [5] has been done towards Step 2. In this paper, we will discuss the component ordering in Step 1 only. In the literature, some methods have been suggested to determine the component order. For example, the components are sorted according to their non-gaussianity [6]. Back and Trappenberg [2] suggested to select a subset of the components based on the mutual information between the observations and the individual components, which also provides another way to order the components. Furthermore, the paper [3] decides the component order according to the norm of each individual component. In these existing methods, the component order is determined based on each individual component only. However, we notice that the observed series are actually in#uenced by several components, whose individually decided optimum order in a sense is generally no longer optimum as a whole from the viewpoint of analyzing the observed series. It

3 Y.-m. Cheung, L. Xu / Neurocomputing 41 (2001) 145} may therefore be more helpful to consider the joint contributions of the components to the time series in performing ordering. In this paper, we propose an alternative ordering scheme to arrange the components according to their joint contributions in data reconstruction. Under the circumstances, the component ordering naturally leads to a typical combinatorial optimization problem, whereby the optimum order is decided under the reconstruction criterion. Moreover, to avoid exhaustive search, we also propose a sub-optimum search algorithm called Testing-and-Acceptance (TnA), which determines the order by sequentially testing each independent component in the series' reconstruction before accepting it. Although this algorithm may introduce some arti"cial ordering, it can still give satisfactory results in most cases. 2. ICA in time-series analysis 2.1. ICA model We suppose that the observed k time series x (t), x (t),2, x (t) are an instantaneous linear mixture of unknown mutually independent components y (t), y (t),2, y (t). The observed series can therefore be modeled in a matrix form x(t)"ay(t), 14t4N, (1) where y(t)"[y (t), y (t),2, y (t)], x(t)"[x (t), x (t),2, x (t)], and A is an unknown kk mixing matrix Extraction of independent components Many existing ICA techniques, e.g. INFORMAX [4], MMI [1], etc., can recover those independent components y(t) in Eq. (1) up to an unknown constant and a permutation of indices through a de-mixing matrix W with y( (t)"wx(t)"way(t)"pλy(t), 14t4N, (2) where y( (t)"[y( (t), y( (t), 2,y( (t)] is an estimate of y( (t), P is a permutation matrix and Λ is a diagonal matrix. W can be tuned by one of those existing ICA algorithms. This paper chooses Learned Parametric Mixture Based ICA algorithm (LPM) [9,10] based upon the fact that it can separate any combination of super- Gaussian and sub-gaussian signals. The details of LPM can be referred to in the relevant papers. 3. Independent component ordering under data reconstruction criterion Given k independent components y( (t) by Eq. (2), we determine a list, which shows the components' order according to their subscripts in the positions of.

4 148 Y.-m. Cheung, L. Xu / Neurocomputing 41 (2001) 145} Independent component ordering Considering the ith time series x (t), we denote the contribution of component y( to the reconstruction of x as u( (t)"= y( (t), 14j4k, (3) where = denotes the (i, j)th element of the matrix W. Thus, the reconstruction of x by using the "rst m independent components under a speci"c list is given by x( (t)" u( (t), with q(r): rth element of. Moreover, we denote Q(x,x( ) to be the corresponding reconstruction error under a given measure. Therefore, the cumulative data reconstruction error with 14m4k is given by J " J (m)" Q(x,x( ). (4) In this paper, we let Q be the Relative Hamming Distance (RHD) function based on the consideration that the trend of a time series may be mostly controlled by the underlying independent components. That is, where and Q(x, x( )"RHD(x, x( )" 1 N!1 [R (t)!rk (t)], (5) R (t)"sign[x (t#1)!x (t)], RK (t)"sign[x( (t#1)!x( (t)], (6) if r'0, sign(r)"1 0 if r"0,!1 otherwise. Hence, the optimum order list H under this Q-measure criterion is H "argmin J Sub-optimum search approach Since the exhaustive search for H out of all k! possible values is quite timeconsuming, we here propose a fast approximate search algorithm called Testing-and- Acceptance (TnA) to "nd a sub-optimum order of independent components. The basic procedure of the TnA algorithm is given as follows: from the set of k independent components, we pick y( as the last one in the ordering, which makes the (7)

5 Y.-m. Cheung, L. Xu / Neurocomputing 41 (2001) 145} Table 1 The RHD reconstruction error between the USD}AUD series and the reconstructions by, respectively, using the "rst m independent components as listed in H ( KH ) and m J H (m)"j KH (m) J (m) RHD error between x and the corresponding reconstruction from those y( minimized. Then, we remove this independent component from the component set. Next, we repeat the same operations on the remaining y(, and select the second-last component,2, and so forth. As a result, we get the k independent components in an order. Note that this TnA algorithm only involves k(k#1)/2!1 reconstruction steps in comparison with the exhaustive search that requires (k#1)! steps. Hence, TnA approach can save considerable computing costs when k is large. The speci"cation of this algorithm is given as follows: Step 1. Given a component subscript set Z" j 14j4k, let n"1, and the order list of independent components "( ). Step 2. For each j3z, let v (t)" u( (t), 14t4N (8) and select the subscript β"argmin RHD(x, v ) as the nth element of Then, let. Z"Z!β. Step 3. If ZO, let n"n#1, and go to Step 2; otherwise go to Step 4. Step 4. Let ĶH", where KH is an estimate of H, and denotes the inverse order of, e.g., if "(3, 2, 4, 1), "(1, 4, 2, 3). Then stop. Note that the ĶH obtained by the TnA arranges the k independent components in descending order. 4. Simulation results We demonstrated the performance of our proposed approach on the foreign exchange rate series, presenting the results of the norm method [3] as well for comparison Experimental data In the experiments, we used six foreign exchange rates, which are US Dollar (USD) versus Australian Dollar (AUD), French Franc, Swiss Franc, German Mark,

6 150 Y.-m. Cheung, L. Xu / Neurocomputing 41 (2001) 145}152 Fig. 1. The observed series of USD*AUD (**) and the reconstructed series (} }}}) by only using (a) y( (t) ; (b) y( (t). Note that the observed series has been normalized to zero mean in accordance with the requirement of the LPM algorithm.

7 British Pound, and Japanese Yen, respectively, from 26 November 1991 to 10 August Experimental results Y.-m. Cheung, L. Xu / Neurocomputing 41 (2001) 145} To save space, we chose the series of USD versus AUD as an example. The order given by the exhaustive search (ExS), TnA and norm method, respectively, is ExS: H"(5, 3, 1, 6, 4, 2), TnA: ĶH"(5, 3, 1, 6, 4, 2), L norm: "(1, 5, 3, 6, 4, 2). In this example, TnA yielded the same component order as ExS but using much less computing costs. Table 1 shows the RHD reconstruction error between the USD}AUD series and the reconstructions by, respectively, using the "rst m independent components as listed in H ( KH ) and We found that the reconstruction error. by using the component y( only is , which is much smaller than that by solely using y(. Fig. 1 shows the reconstructed series by using y( and y(, respectively. It can be seen that the reconstruction from the sole component y( has a similar variation of the USD}AUD series, but that from y( has totally lost the trend of the observed series. In other words, the independent component y( can dominate the trend of USD}AUD series, but y( cannot. Similarly, we can also show that the component order (5, 3) is better than (1, 5). That is, our proposed method provides a better component order under the series reconstruction criterion. 5. Conclusion We have proposed a technique to order the independent components under a data reconstruction criterion, which leads the component ordering to a typical combinatorial optimization problem. Hence, the optimum order can be determined through an exhaustive search in principle. To save searching costs, we have also presented a sub-optimum ordering algorithm TnA. The accompanying experiments have shown that our approach outperforms the norm method in the reconstruction criterion. References [1] S. Amari, A. Cichocki, H. Yang, A new learning algorithm for blind signal separation, Adv. Neural Inform. Processing System 8 (1996) 757}763. [2] A.D. Back, T.P. Trappenberg, Input variable selection using independent component analysis, Proceedings of International Joint Conference on Neural Networks, Vol. 2, 1999, pp. 989}992. [3] A.D. Back, A.S. Weigend, A "rst application of independent component analysis to extracting structure from stock returns, Int. J. Neural Systems 8 (4) (1997) 473}484.

8 152 Y.-m. Cheung, L. Xu / Neurocomputing 41 (2001) 145}152 [4] A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution, Neural Computation 7 (1995) 1129}1159. [5] Y.M. Cheung, L. Xu, An empirical method to select dominant independent components in ICA time series analysis, Proceedings of 1999 International Joint Conference on Neural Networks (IJCNN'99), Vol. 6, Washington, DC, July 10}16, 1999, pp. 3883}3887. [6] A. HyvaK rinen, Survey on independent component analysis, Neural Computing Surveys 2 (1999) 94}128. [7] K. Kiviluoto, E. Oja, Independent component analysis for parallel "nancial time series, Proceedings of the Fifth International Conference on Neural Information Processing (ICONIP'98), 1998, pp. 895}898. [8] J. Moody, L. Wu, What is the &true price'? * State space models for high frequency FX data, in: A.S. Weigend, Y. Abu-Mostafa, A.-Paul N. Refenes (Eds.), Decision Technologies for Financial Engineering * Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets (NNCM'96), World Scienti"c, Singapore, 1997, pp. 346}358. [9] L. Xu, C.C. Cheung, S.I. Amari, Learned parametric mixture based ICA algorithm, Neurocomputing 22 (1998) 69}80. [10] L. Xu, C.C. Cheung, H.H. Yang, S.I. Amari, Independent component analysis by the informationtheoretic approach with mixture of density, Proceedings of 1997 IEEE International Conference on Neural Networks (IJCNN'97), Vol. 3, 1997, pp. 1821}1826.

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