Markov Chains application to the financial-economic time series prediction

2011/11/22 by Vladimir Soloviev, Soloviev, Vladimir, Vladimir Saptsin +3
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Complex Systems and Time Series Analysis #Data Analysis #FOS: Economics and business #FOS: Physical sciences #Neural Networks and Applications #Statistical Finance (q-fin.ST) #Statistics and Probability (physics.data-an) #Time Series Analysis and Forecasting #physics.data-an #q-fin.ST

paper · pdf · doi:10.48550/arxiv.1111.5254

24 pages, 13 figures

arxiv created 2011/11/22 · openalex publication_date 2011/11/22 · arxiv updated 2011/11/23 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28

Abstract

In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of aftereffect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different frequencies of the series. The prediction results for world's stock market indices is presented.

Related

• Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods
• Financial Time Series Data Processing for Machine Learning
• MegazordNet: combining statistical and machine learning standpoints for time series forecasting
• Indexed Markov Chains for financial data: testing for the number of\n states of the index process
• Using Artificial Market Models to Forecast Financial Time-Series