Forecasting Nonlinear Nonstationary Processes in Machine Learning Task

The article discusses the features of the solving the forecasting problems using machine learning techniques. The issues of accounting and correctly processing non-linear non-stationary processes in the problems of modeling and forecasting time series in various areas are considered. The analysis of stages and methods for solving machine learning problems. As an example, consider the problem of predicting currency pairs based on historical data. A comparative analysis of the normalization methods in data clustering is given. For the six currency pairs a short-term forecast is proposed.

[1]  Peter Bidyuk,et al.  Analysis of Uncertainty Types for Model Building and Forecasting Dynamic Processes , 2017 .

[2]  T. Bollerslev,et al.  Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon , 1999 .

[3]  Genaro Sucarrat,et al.  Automated Model Selection in Finance: General-to-Speciflc Modelling of the Mean, Variance and Density ⁄ , 2010 .

[4]  M. Pesaran,et al.  Optimal Forecasts in the Presence of Structural Breaks , 2011 .

[5]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[6]  Max A. Little,et al.  Highly comparative time-series analysis: the empirical structure of time series and their methods , 2013, Journal of The Royal Society Interface.

[7]  Genaro Sucarrat,et al.  Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks , 2018 .

[8]  Carol Alexander,et al.  Quantitative methods in finance , 2008 .

[9]  Stavros Degiannakis,et al.  ARCH Models for Financial Applications , 2010 .

[10]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[11]  H. Iemoto.  Modelling the persistence of conditional variances , 1986 .

[12]  Andreas Pick,et al.  Optimal Forecasts in the Presence of Structural Breaks , 2011 .

[13]  Rob J. Hyndman,et al.  Large-Scale Unusual Time Series Detection , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[14]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[15]  Nick S. Jones,et al.  Highly Comparative Feature-Based Time-Series Classification , 2014, IEEE Transactions on Knowledge and Data Engineering.

[16]  M. McAleer,et al.  The structure of dynamic correlations in multivariate stochastic volatility models , 2009 .

[17]  T. Bollerslev,et al.  A CONDITIONALLY HETEROSKEDASTIC TIME SERIES MODEL FOR SPECULATIVE PRICES AND RATES OF RETURN , 1987 .

[18]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[19]  Peter Bidyuk,et al.  The Methods Bayesian Analysis of the Threshold Stochastic Volatility Model , 2018, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP).

[20]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .