A model-free feature extraction procedure for interval-valued time series prediction

We present a novel feature extraction procedure to predict interval-valued time series by combing transfer learning and imaging approaches. Initially, we represent interval-valued time series as a bivariate point-valued time series, and then transform each time series into images by employing imaging approaches such as recurrence plot, gramian angular summation/difference field, and Markov transition field, thereby obtaining an image dataset containing four classes. Based on this dataset, we train multiple candidate feature extraction networks, specifically ResNet with varying layers. Then we choose the penultimate layer of the feature extraction networks to extract the most relevant features from the images. To formulate prediction, we integrate the extracted features into a traditional prediction model. The proposed method are evaluated on three data-generating processes as well as the S&P 500 index, and the experimental results demonstrate a notable improvement in prediction performance compared to existing methods.