A time series trend prediction algorithm at multiple time scales based on novel feature model was proposed to solve the trend prediction problem of stock and fund time series data. Firstly, a feature tree with multiple time scales of features was extracted from original time series, which described time series with the characteristics of the series in each level and relationship between levels. Then, the hidden states in feature sequences were extracted by clustering. Finally, a Multiple Time Scaled Trend Prediction Algorithm (MTSTPA) was designed by using Hidden Markov Model (HMM) to simultaneously predict the trend and length of the trends at different scales. In the experiments on real stock datasets, the prediction accuracy at every scale are more than 60%. Compared with the algorithm without using feature tree, the model using the feature tree is more efficient, and the accuracy is up to 10 percentage points higher at a certain scale. At the same time, compared with the classical Auto-Regressive Moving Average (ARMA) model and pattern-based Hidden Markov Model (PHMM), MTSTPA performs better, verifying the validity of MTSTPA.

After analyzing the advantages and disadvantages of good point genetic algorithm, the crossover operation of Genetic Algorithm (GA) was redesigned by using the theory and methods of Uniform Design Sampling (UDS). Then an improved GA based on UDS was presented. In combination with statistical criteria, the new algorithm was used for variable and transformation simultaneous selection in solving regression model selection problem. The results of simulation show a good improvement in solution quality, stability and other various indices.