Host: Japan SOciety for Fuzzy Theory and intelligent informatics
Co-host: The Korea Fuzzy Logic and Intelligent Systems Society, IEEE Computational Intelligence Society, The International Fuzzy Systems Association, 21th Century COE Program "Creation of Agent-Based Social Systems Sciences"
We have developed a new analysis method of time sequential data based on moving averages. Firstly, we apply the optimal moving average that makes the residual term being independent. Then, we introduce so-called the super moving averages of this optimal moving average, and we estimate underlying potential forces by comparing the differences of these quantities for various time scales [1]. This method has been successfully applied for analysis of market prices. For the check of validity of this method we have applied this method to pure random walks. It is confirmed that the estimated potential forces are very week in such cases. For real market data the distribution is quite different from this theoretical case, sometimes the observed potential forces are clearly much stronger than random cases. In most cases the observed potential functions are nearly symmetric and in such cases we can only predict the speed of diffusion. However, there are cases that the potential function becomes asymmetric when we can predict the averaged direction of price changes.
