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Time series forecasting is a crucial component of many importantapplications, ranging from forecasting the stock markets to energy loadprediction. The high-dimensionality, velocity and variety of the data collectedin these applications pose significant and unique challenges that must becarefully addressed for each of them. In this work, a novel Temporal LogisticNeural Bag-of-Features approach, that can be used to tackle these challenges,is proposed. The proposed method can be effectively combined with deep neuralnetworks, leading to powerful deep learning models for time series analysis.However, combining existing BoF formulations with deep feature extractors posesignificant challenges: the distribution of the input features is notstationary, tuning the hyper-parameters of the model can be especiallydifficult and the normalizations involved in the BoF model can causesignificant instabilities during the training process. The proposed method iscapable of overcoming these limitations by a employing a novel adaptive scalingmechanism and replacing the classical Gaussian-based density estimationinvolved in the regular BoF model with a logistic kernel. The effectiveness ofthe proposed approach is demonstrated using extensive experiments on alarge-scale financial time series dataset that consists of more than 4 millionlimit orders.

Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data