Application of Hidden Markov Model in Financial Time Series Data

Abstract

Financial time series have typical characteristics such as outliers, trends, and mean reversion. The existence of outliers will affect the effectiveness of the unknown parameter estimation in the financial time series forecasting model, so that the forecasting error of the model will be larger. Quantitative forecasting methods are divided into causal forecasting method and time series forecasting method. The causal forecasting method uses the causal relationship between the predictor variable and other variables to predict, and the time series forecasting method infers the future value of the predictor variable based on the structure of the historical data of the predictor. Therefore, this paper proposes a hidden Markov model prediction method based on the observation vector sequence, which can simultaneously consider the influence of the variable sequence structure and related factors.

Modeling matrix variate time series via hidden Markov models with skewed emissions

“…In essence, HMMs can be viewed as an extension of finite mixture models that allow for data dependence [12]. Applications of HMM can be found in various fields including speech recognition [13], motion time series data [14], stock market data [15], environmental science [16,17], finance [18], and medicine [19]. To naturally fit the time series data in its matrix-variate structure, HMMs with matrix-variate emissions are recently studied literature.…”

Retracted: Application of Hidden Markov Model in Financial Time Series Data

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”