Learning Structures in an Economic Time-Series for Forecasting Applications

The chapter introduces a machine learning approach to knowledge acquisition from a time-series by incorporating three fundamental steps. The first step deals with segmentation of the time-series into time-blocks of non-uniform length with distinguishable characteristics from their neighbours. The second step groups structurally similar time-blocks into clusters by an extension of the DBSCAN algorithm to incorporate multilevel hierarchical clustering. The third step involves representation of the time-series by a special type of automaton with no fixed start or end states. The states in the proposed automaton represent (horizontal) partitions of the time-series, while the cluster centres obtained in the second step are used as input symbols to the states. The state-transitions here are attached with two labels: probability of the transition due the input symbol at the current state and the expected time required for the transition. Once an automaton is built, the knowledge acquisition (training) phase is over. During the test phase, the automaton is consulted to predict the most probable sequence of symbols at a given starting state and the approximate time required (within user-defined margin) to reach a user-defined target state with its probability of occurrence. Test phase prediction accuracy being high over 90%, the proposed prediction can be utilized for trading and investment in stock market.

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