Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data

•Logistic Neural Bag-of-Features are employed for financial time series analysis.•The proposed method can be efficiently used in deep learning architectures.•An adaptive scaling method is proposed to ensure the smooth flow of information.•A logistic kernel is used to estimate the feature vector densities.•The proposed method outperforms the competitive methods on a large-scale dataset.