Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency Limit Order Book Data

Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis

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    Time-series forecasting has various applications in a wide range of domains, e.g., forecasting stock markets using limit order book data. Limit order book data provide much richer information about the behavior of stocks than its price alone, but also bear several challenges, such as dealing with multiple price depths and processing very large amounts of data of high dimensionality, velocity, and variety. A well-known approach for efficiently handling large amounts of high-dimensional data is the bag-of-features (BoF) model. However, the BoF method was designed to handle multimedia data such as images. In this paper, a novel temporal-aware neural BoF model is proposed tailored to the needs of time-series forecasting using high frequency limit order book data. Two separate sets of radial basis function and accumulation layers are used in the temporal BoF to capture both the short-term behavior and the long-term dynamics of time series. This allows for modeling complex temporal phenomena that occur in time-series data and further increase the forecasting ability of the model. Any other neural layer, such as feature transformation layers, or classifiers, such as multilayer perceptrons, can be combined with the proposed deep learning approach, which can be trained end-to-end using the back-propagation algorithm. The effectiveness of the proposed method is validated using a large-scale limit order book dataset, containing over 4.5 million limit orders, and it is demonstrated that it greatly outperforms all the other evaluated methods.
    Original languageEnglish
    Pages (from-to)774-785
    Number of pages12
    JournalIEEE Transactions on Emerging Topics in Computational Intelligence
    Issue number6
    Early online date9 Oct 2018
    Publication statusPublished - Dec 2020
    Publication typeA1 Journal article-refereed


    • Feature extraction
    • Forecasting
    • Data models
    • Machine learning
    • Predictive models
    • Task analysis
    • Hidden Markov models
    • Limit order book
    • neural networks
    • bag-of-features

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