A Prediction Model for High-Frequency Financial Time Series

A wide number of sophisticated models have been proposed in the literature to solve prediction problems. However, a drawback arises in the particular case of financial prediction problems and called the random walk dilemma (RWD). In this context, the concept of time phase adjustment can be used to overcome the problem for daily-frequency financial time series. However, the fast evolution of trading platforms increased the frequency for performing operations in the stock market for fractions of seconds, which makes the analysis of high-frequency financial time series very important in this current scenario. In this way, this paper presents a model, called the increasing decreasing linear neuron (IDLN), to predict high-frequency financial time series from the Brazilian stock market. Besides, a descending gradient-based method with automatic time phase adjustment is presented for the design of the proposed model, and the obtained results overcame those obtained by established prediction models in the literature.