Conference Paper

HMM Training by Using a Self-Organizing Map for Time Series Prediction

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For individual investors to analyze changes in stock prices and foreign exchange rates to predict future trends is an extremely difficult task. In order to enable such predictions based on the analysis of time series data sets, this research proposes a method combining a Self-Organizing Map with a Hidden Markov Model and provides evidence for its usefulness in making predictions.

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In this paper, a neural network based foreign exchange rates forecasting method is discussed. Neural networks with time series and technical indicators as inputs are built to capture the underlying "rules" of the movement in currency exchange rates. Before using historical data to train the neural networks, the traditional R/S analysis is used to test the "efficiency" of each market. The study shows that without the use of extensive market data or knowledge, useful prediction can be made and significant paper profit can be achieved with simple technical indicators. 1 Introduction Since 1973, with the abandonment of the fixed foreign exchange rates and the implementation of the floating exchange rate system by industrialized countries, researchers have been striving for an explanation of the movement of exchange rates. Thus, many kinds of forecasting methods are developed by thousands of researches and experts. Technical and fundamental analysis are among the major forecasting methods...
The rapid progress in and the expanding complexity of information and technology systems have made data analysis increasingly relevant. Data having a variety of elements are complex, and making very difficult to evaluate a state of a model from observed data generated probabilistically by the model. To evaluate these hidden states, we propose Spherical-Self Organizing Map (S-SOM) with a Hidden Markov Model (HMM) that infers such hidden states.
Conference Paper
In stock market, prediction of the stock price fluctuation has been important for the investors. However, it is hard for the beginner investors to predict the stock price changes due to the difficulty of estimating a company's state makes. To estimate company's state, we propose the suitable model using Spherical-Self Organizing Map that is integrated frequency vector and Hidden Markov Model to estimate hidden state from the time series data. On this paper, the power company stock price movements are used as the time series data, and we also show a result using improved the Self Organizing Map.
The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed
Conference Paper
In modern society, the more complex information and technology become, the more important data analysis become. In particular, a data, which has a variety of elements, is complex, and it is extremely difficult to estimate the state which generates data from observed data. To handle those hidden states, we propose an appropriate model using Spherical-Self Organizing Map (S-SOM) with Hidden Markov Model (HMM) which can estimate the hidden state.
Conference Paper
Recently next generation sequencing techniques have begun to produce huge amounts of sequencing data. To analyze these data, an efficient method that can handle large amounts of information is required. In this paper, we proposed a method for classifying sets of DNA sequences by using a hidden Markov model self-organizing map. For this purpose, a learning algorithm that requires low computational costs was developed. The availability of this method was examined in experiments classifying DNA sequences of various types of genes.
The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics. The most extensive applications, exemplified in this paper, can be found in the management of massive textual databases and in bioinformatics. The SOM is related to the classical vector quantization (VQ), which is used extensively in digital signal processing and transmission. Like in VQ, the SOM represents a distribution of input data items using a finite set of models. In the SOM, however, these models are automatically associated with the nodes of a regular (usually two-dimensional) grid in an orderly fashion such that more similar models become automatically associated with nodes that are adjacent in the grid, whereas less similar models are situated farther away from each other in the grid. This organization, a kind of similarity diagram of the models, makes it possible to obtain an insight into the topographic relationships of data, especially of high-dimensional data items. If the data items belong to certain predetermined classes, the models (and the nodes) can be calibrated according to these classes. An unknown input item is then classified according to that node, the model of which is most similar with it in some metric used in the construction of the SOM. A new finding introduced in this paper is that an input item can even more accurately be represented by a linear mixture of a few best-matching models. This becomes possible by a least-squares fitting procedure where the coefficients in the linear mixture of models are constrained to nonnegative values.
This work contains a theoretical study and computer simulations of a new self-organizing process. The principal discovery is that in a simple network of adaptive physical elements which receives signals from a primary event space, the signal representations are automatically mapped onto a set of output responses in such a way that the responses acquire the same topological order as that of the primary events. In other words, a principle has been discovered which facilitates the automatic formation of topologically correct maps of features of observable events. The basic self-organizing system is a one- or two-dimensional array of processing units resembling a network of threshold-logic units, and characterized by short-range lateral feedback between neighbouring units. Several types of computer simulations are used to demonstrate the ordering process as well as the conditions under which it fails.
A quarter-century quest hasn't found the elusive links between economic fundamentals and currency values. ; The U.S. dollar has been losing value against several major currencies this decade. Since 2001-02, the U.S. currency has fallen about 50 percent against the euro, 40 percent against the Canadian dollar and 30 percent against the British pound .
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