Giovanis, Eleftherios (2008): Applications of Least Mean Square (LMS) Algorithm Regression in TimeSeries Analysis.

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Abstract
In this paper we present a very brief description of least mean square algorithm with applications in timeseries 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.
Item Type:  MPRA Paper 

Original Title:  Applications of Least Mean Square (LMS) Algorithm Regression in TimeSeries Analysis 
Language:  English 
Keywords:  LMS; Least Mean Square Algorithm; MATLAB; timeseries; stock returns; gross domestic product; forecast 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods; Simulation Methods C  Mathematical and Quantitative Methods > C6  Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C63  Computational Techniques; Simulation Modeling C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics C  Mathematical and Quantitative Methods > C2  Single Equation Models; Single Variables > C22  TimeSeries Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models 
Item ID:  24658 
Depositing User:  Eleftherios Giovanis 
Date Deposited:  29. Aug 2010 09:14 
Last Modified:  11. Feb 2013 23:11 
References:  Gabor, D. (1954). Communication theory and cybernetics. IRE Transactions on Circuit Theory, Vol. CT1, pp. 1931 Hayes, M. H. (1996). Statistical Signal Processing and Modeling, Wiley, 1996 Haykin, S. (1996). Adaptive Filter Theory, 3rd Edition, Prentice Hall, 1996 Widrow, B., and M.E. Hoff, Jr. (1960). Adaptive switching circuits. IRE WESCON Convention Record, pp. 96104 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/24658 