7,392 research outputs found

    Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market

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    The original research question here is given by marketers in general, i.e., how to explain the changes in the desired timescale of the market. Tangled String, a sequence visualization tool based on the metaphor where contexts in a sequence are compared to tangled pills in a string, is here extended and diverted to detecting stocks that trigger changes in the market and to explaining the scenario of contextual shifts in the market. Here, the sequential data on the stocks of top 10 weekly increase rates in the First Section of the Tokyo Stock Exchange for 12 years are visualized by Tangled String. The changing in the prices of stocks is a mixture of various timescales and can be explained in the time-scale set as desired by using TS. Also, it is found that the change points found by TS coincided by high precision with the real changes in each stock price. As TS has been created from the data-driven innovation platform called Innovators Marketplace on Data Jackets and is extended to satisfy data users, this paper is as evidence of the contribution of the market of data to data-driven innovations.Comment: 16 pages and 7 figures. The author started to write this paper as an extension of the paper [20] in the reference list, but the content came to be changed substantially, not by only minor extension but to a new pape

    Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting

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    Spatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining. It facilitates many real world applications such as precipitation nowcasting, citywide crowd flow prediction and air pollution forecasting. Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase. That is because Seq2Seq models minimise single step errors only during training, however the entire sequence has to be generated during the inference phase which generates a discrepancy between training and inference. In this work, we propose a novel curriculum learning based strategy named Temporal Progressive Growing Sampling to effectively bridge the gap between training and inference for spatio-temporal sequence forecasting, by transforming the training process from a fully-supervised manner which utilises all available previous ground-truth values to a less-supervised manner which replaces some of the ground-truth context with generated predictions. To do that we sample the target sequence from midway outputs from intermediate models trained with bigger timescales through a carefully designed decaying strategy. Experimental results demonstrate that our proposed method better models long term dependencies and outperforms baseline approaches on two competitive datasets.Comment: ECAI 2020 Accepted, preprin

    Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

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    Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1

    A phenomenological cluster-based model of Ca2+ waves and oscillations for Inositol 1,4,5-trisphosphate receptor (IP3R) channels

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    Clusters of IP3 receptor channels in the membranes of the endoplasmic reticulum (ER) of many non-excitable cells release calcium ions in a cooperative manner giving rise to dynamical patterns such as Ca2+ puffs, waves, and oscillations that occur on multiple spatial and temporal scales. We introduce a minimal yet descriptive reaction-diffusion model of IP3 receptors for a saturating concentration of IP3 using a principled reduction of a detailed Markov chain description of individual channels. A dynamical systems analysis reveals the possibility of excitable, bistable and oscillatory dynamics of this model that correspond to three types of observed patterns of calcium release -- puffs, waves, and oscillations respectively. We explain the emergence of these patterns via a bifurcation analysis of a coupled two-cluster model, compute the phase diagram and quantify the speed of the waves and period of oscillations in terms of system parameters. We connect the termination of large-scale Ca2+ release events to IP3 unbinding or stochasticity.Comment: 18 pages, 10 figure

    A Center-Median Filtering Method for Detection of Temporal Variation in Coronal Images

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    Events in the solar corona are often widely separated in their timescales, which can allow them to be identified when they would otherwise be confused with emission from other sources in the corona. Methods for cleanly separating such events based on their timescales are thus desirable for research in the field. This paper develops a technique for identifying time-varying signals in solar coronal image sequences which is based on a per-pixel running median filter and an understanding of photon-counting statistics. Example applications to 'EIT Waves' and small-scale dynamics are shown, both using data from the 193 Angstrom channel on AIA. The technique is found to discriminate EIT Waves more cleanly than the running and base difference techniques most commonly used. It is also demonstrated that there is more signal in the data than is commonly appreciated, finding that the waves can be traced to the edge of the AIA field of view when the data are rebinned to increase the signal-to-noise ratio.Comment: 15 pages, 7 Figures, Accepted to Journal of Space Weather and Space Climate; version 2 has slight text changes and updated movie URL
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