Gold Risk Prediction Model Based On Chaotic Time Series

Finance is the core of the modern market economy, complete stability of the financial system is the key to sustained economic and social development. With the emergence of financial markets more and more incidents, resulting in these internal causes mutations increasingly cause for concern, the current mainstream analysis approach is to study the internal mechanism of the system based on chaos theory. Gold market as a financial market is an important part of the chaos theory of systemic risk prediction models for its study is the main purpose of this article.The main contents are as follows:(1) Chaos gold market system to discriminate, through verification and deterministic nonlinear gold verification systems have come to China gold market chaos conclusions. Gold market for the subsequent chaos theory-based research provides in-depth theoretical basis;(2) Proposed a method based on chaos theory recurrence plot of prime time series of the risk characteristics of representation. Proposed three gold risk status corresponding recursive graph pattern, form risk knowledge base;(3) The use of BP neural network and Elman neural network to predict the gold price time series. Experimental results show that the gold price forecast Elman higher precision, more suitable for the gold price time series forecasting;(4) The distance metric unrelated field of time series classification DTW (Dynamic Time Warping) and RPCD (Recurrence Patterns Compression Distance) of gold price category. Experimental public data sets show, RPCD classification better than DTW.Application of recursive diagram analysis method in the gold market in the gold price represents the risk characteristics of time series, based on a recursive graph pattern to deduce the presence of law gold price risks. These findings help to further analysis of the gold market risk occurrence, in order to take timely and appropriate early warning measures.