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Applications of Least Mean Square (LMS) Algorithm Regression in Time-Series Analysis

Author

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  • Giovanis, Eleftherios

Abstract

In this paper we present a very brief description of least mean square algorithm with applications in time-series analysis of economic and financial time series. We present some numerical applications; forecasts for the Gross Domestic Product growth rate of UK and Italy, forecasts for S&P 500 stock index returns and finally we examine the day of the week effect of FTSE 100 for a short period. A full programming routine written in MATLAB software environment is provided for replications and further research applications.

Suggested Citation

  • Giovanis, Eleftherios, 2008. "Applications of Least Mean Square (LMS) Algorithm Regression in Time-Series Analysis," MPRA Paper 24658, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:24658
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    File URL: https://mpra.ub.uni-muenchen.de/24658/1/MPRA_paper_24658.pdf
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    More about this item

    Keywords

    LMS; Least Mean Square Algorithm; MATLAB; time-series; stock returns; gross domestic product; forecast;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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