DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift

In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribution (OOD) LOB data is challenging since distributional shifts are unlabeled in current publicly available LOB datasets. Therefore, it is critical to build a synthetic LOB dataset with labeled OOD samples serving as a testbed for developing models that generalize well to unseen scenarios. In this work, we utilize a multi-agent market simulator to build a synthetic LOB dataset, named DSLOB, with and without market stress scenarios, which allows for the design of controlled distributional shift benchmarking. Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods. Our results reflect the need for increased researcher efforts to develop algorithms with robustness to distributional shifts in high-frequency time series data.Comment: 11 pages, 5 figures, already accepted by NeurIPS 2022 Distribution Shifts Worksho

Phylogenetic inference of calyptrates, with the first mitogenomes for Gasterophilinae (Diptera: Oestridae) and Paramacronychiinae (Diptera: Sarcophagidae)

The complete mitogenome of the horse stomach bot fly Gasterophilus pecorum (Fabricius) and a near-complete mitogenome of Wohlfahrt's wound myiasis fly Wohlfahrtia magnifica (Schiner) were sequenced. The mitogenomes contain the typical 37 mitogenes found in metazoans, organized in the same order and orientation as in other cyclorrhaphan Diptera. Phylogenetic analyses of mitogenomes from 38 calyptrate taxa with and without two non-calyptrate outgroups were performed using Bayesian Inference and Maximum Likelihood. Three sub-analyses were performed on the concatenated data: (1) not partitioned; (2) partitioned by gene; (3) 3rd codon positions of protein-coding genes omitted. We estimated the contribution of each of the mitochondrial genes for phylogenetic analysis, as well as the effect of some popular methodologies on calyptrate phylogeny reconstruction. In the favoured trees, the Oestroidea are nested within the muscoid grade. Relationships at the family level within Oestroidea are (remaining Calliphoridae (Sarcophagidae (Oestridae, Pollenia + Tachinidae))). Our mito-phylogenetic reconstruction of the Calyptratae presents the most extensive taxon coverage so far, and the risk of long-branch attraction is reduced by an appropriate selection of outgroups. We find that in the Calyptratae the ND2, ND5, ND1, COIII, and COI genes are more phylogenetically informative compared with other mitochondrial protein-coding genes. Our study provides evidence that data partitioning and the inclusion of conserved tRNA genes have little influence on calyptrate phylogeny reconstruction, and that the 3rd codon positions of protein-coding genes are not saturated and therefore should be included

Neuro-Inspired Hierarchical Multimodal Learning

Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Distinct from most traditional fusion models that aim to incorporate all modalities as input, our model designates the prime modality as input, while the remaining modalities act as detectors in the information pathway. Our proposed perception model focuses on constructing an effective and compact information flow by achieving a balance between the minimization of mutual information between the latent state and the input modal state, and the maximization of mutual information between the latent states and the remaining modal states. This approach leads to compact latent state representations that retain relevant information while minimizing redundancy, thereby substantially enhancing the performance of downstream tasks. Experimental evaluations on both the MUStARD and CMU-MOSI datasets demonstrate that our model consistently distills crucial information in multimodal learning scenarios, outperforming state-of-the-art benchmarks

Frequency-domain MLPs are More Effective Learners in Time Series Forecasting

Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many sophisticated architectures based on RNNs, GNNs, or Transformers, another kind of approaches based on multi-layer perceptrons (MLPs) are proposed with simple structure, low complexity, and {superior performance}. However, most MLP-based forecasting methods suffer from the point-wise mappings and information bottleneck, which largely hinders the forecasting performance. To overcome this problem, we explore a novel direction of applying MLPs in the frequency domain for time series forecasting. We investigate the learned patterns of frequency-domain MLPs and discover their two inherent characteristic benefiting forecasting, (i) global view: frequency spectrum makes MLPs own a complete view for signals and learn global dependencies more easily, and (ii) energy compaction: frequency-domain MLPs concentrate on smaller key part of frequency components with compact signal energy. Then, we propose FreTS, a simple yet effective architecture built upon Frequency-domain MLPs for Time Series forecasting. FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components. The above stages operated on both inter-series and intra-series scales further contribute to channel-wise and time-wise dependency learning. Extensive experiments on 13 real-world benchmarks (including 7 benchmarks for short-term forecasting and 6 benchmarks for long-term forecasting) demonstrate our consistent superiority over state-of-the-art methods