How can we efficiently and accurately factorize multi-source data in dynamic and real-time environments? Coupled matrix-tensor factorization (CMTF) is a powerful tool for such tasks, but existing methods often struggle with scalability, particularly when dealing with continuously streaming data. Traditional CMTF approaches, while effective at capturing complex relationships, suffer from computational inefficiencies and the need for retraining as new data arrive. Moreover, many techniques fail to properly incorporate the inherent temporal characteristics of the data, which could significantly enhance both accuracy and convergence speed.

How can we accurately recognize overlapping entity spans in text while effectively capturing global context among spans? Nested Named Entity Recognition (nested NER) becomes challenging in the presence of nested or overlapping entity spans. Traditional span-based methods enumerate all possible spans, resulting in high computational costs and severe label imbalance from excessive negative spans which are non-entities. Additionally, they often fail to fully capture global context among overlapping entities.

How can we utilize historical stock prices for accurate stock movement prediction? There have been several attempts to predict the movement of a stock using historical stock prices. However, due to the erratic nature of the stock market, it is difficult to accurately predict the future trajectory of stocks. Existing works are limited in capturing complex dependencies that exist within the stock data.In this paper, we propose ZoomStock, an accurate method for stock movement prediction. ZoomStock captures complex patterns in stock price data with multi-scale and multi-domain modeling. First, ZoomStock captures multi-scale patterns by examining short-term, mid-term, and long-term dependencies from data. By feeding multi-scale data into a 1D convolution layer and applying a circular convolution on data, we learn patterns in various scales. Second, ZoomStock leverages multi-domain features to extract diverse patterns from stock data. In addition to finding patterns in time domain, ZoomStock exploits patterns in the frequency domain via Fourier Transform. We conduct extensive experiments on real-world datasets from diverse stock markets to evaluate the effectiveness of ZoomStock. Our results demonstrate that ZoomStock outperforms previous state-of-the-art models by up to 15.7%p in accuracy and 31.7%p in MCC, showcasing its superior predictive capabilities.

How can we accurately decompose a temporal irregular tensor along with a related knowledge graph tensor? The PARAFAC2 decomposition is widely used for analyzing irregular tensors composed of matrices with varying row sizes. Recent advancements in PARAFAC2 methods primarily focus on capturing dynamic features that change over time, since data irregularities often arise from temporal fluctuations. However, these methods often neglect static features, such as knowledge information, which remain constant over time.In this paper, we propose KG-CTF (Knowledge Graph-based Coupled Tensor Factorization), a coupled tensor factorization method designed to capture both dynamic and static features within an irregular tensor. To incorporate knowledge graph tensors as static features, KG-CTF couples an irregular temporal tensor with a knowledge graph tensor that share a common axis. Additionally, KG-CTF employs a relational regularization to capture relationships among the factor matrices of the knowledge graph tensor. For accelerated convergence of the factor matrices, KG-CTF utilizes momentum update techniques. Extensive experiments show that KG-CTF reduces error rates by up to 1.64× compared to existing PARAFAC2 methods.

How can we efficiently analyze a specific time range on an irregular tensor? PARAFAC2 decomposition is widely used when analyzing an irregular tensor which consists of several matrices with different row sizes. A crucial task related to PARAFAC2 decomposition is to analyze sub-tensors corresponding to various time ranges of a given tensor, instead of analyzing the entire tensor. Although many recent works have developed efficient PARAFAC2 decomposition methods, existing PARAFAC2 decomposition methods are inappropriate for addressing various time range queries, as they need to decompose sub-tensors from scratch.

Given a multidimensional array, how can we optimize the computation process for a part of Fourier coefficients? Discrete Fourier transform plays an overarching role in various data mining tasks. Recent interest has focused on efficiently calculating a small part of Fourier coefficients, exploiting the energy compaction property of real-world data. Current methods for partial Fourier transform frequently encounter efficiency issues, yet the adoption of pre-computation techniques within the PFT algorithm has shown promising performance. However, PFT still faces limitations in handling multidimensional data efficiently and requires manual hyperparameter tuning, leading to additional costs.

How can we efficiently and accurately analyze an irregular tensor in a dual-way streaming setting where the sizes of two dimensions of the tensor increase over time? What types of anomalies are there in the dual-way streaming setting? An irregular tensor is a collection of matrices whose column lengths are the same while their row lengths are different. In a dual-way streaming setting, both new rows of existing matrices and new matrices arrive over time. PARAFAC2 decomposition is a crucial tool for analyzing irregular tensors. Although real-time analysis is necessary in the dual-way streaming, static PARAFAC2 decomposition methods fail to efficiently work in this setting since they perform PARAFAC2 decomposition for accumulated tensors whenever new data arrive. Existing streaming PARAFAC2 decomposition methods work in a limited setting and fail to handle new rows of matrices efficiently.

Given a time-series vector, how can we efficiently detect anomalies? A widely used method is to use Fast Fourier transform (FFT) to compute Fourier coefficients, take first few coefficients while discarding the remaining small coefficients, and reconstruct the original time series to find points with large errors. Despite the pervasive use, the method requires to compute all of the Fourier coefficients which can be cumbersome if the input length is large or when we need to perform many FFT operations.

How can we efficiently correlate multiple stocks for accurate stock movement prediction? Stock movement prediction has received growing interest in data mining and machine learning communities due to its substantial impact on financial markets. One way to improve the prediction accuracy is to utilize the correlations between multiple stocks, getting a reliable evidence regardless of the random noises of individual prices. However, it has been challenging to acquire accurate correlations between stocks because of their asymmetric and dynamic nature which is also influenced by the global movement of a market. In this work, we propose DTML (Data-axis Transformer with Multi-Level contexts), a novel approach for stock movement prediction that learns the correlations between stocks in an end-to-end way. DTML makes asymmetric and dynamic correlations by a) learning temporal correlations within each stock, b) generating multi-level contexts based on a global market context, and c) utilizing a transformer encoder for learning inter-stock correlations. DTML achieves the state-of-the-art accuracy on six datasets collected from various stock markets from US, China, Japan, and UK, making up to 13.8%p higher profits than the best competitors and the annualized return of 44.4% on investment simulation.