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Found 27 papers, 1 papers with code
Random Projection Layers for Multidimensional Time Series Forecasting
All-Multi-Layer Perceptron (all-MLP) mixer models have been shown to be effective for time series forecasting problems.
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
In this work we introduce a domain agnostic counterfactual explanation technique to produce explanations for time series anomalies.
Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets.
Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights
To facilitate this investigation, we introduce a CTSR benchmark dataset that comprises time series data from a variety of domains, such as motion, power demand, and traffic.
Ego-Network Transformer for Subsequence Classification in Time Series Data
The ego-networks of all subsequences collectively form a time series subsequence graph, and we introduce an algorithm to efficiently construct this graph.
Time Series Synthesis Using the Matrix Profile for Anonymization
As a result, unmodified data mining tools can obtain near-identical performance on the synthesized time series as on the original time series.
Sketching Multidimensional Time Series for Fast Discord Mining
In this work, we propose a sketch for discord mining among multi-dimensional time series.
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
FATA-Trans is field- and time-aware for sequential tabular data.
An Efficient Content-based Time Series Retrieval System
A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing.
Multitask Learning for Time Series Data with 2D Convolution
In this paper, we investigate the application of MTL to the time series classification (TSC) problem.
Toward a Foundation Model for Time Series Data
A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks.
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs
Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories.
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series
PRCIS is a distance measure for long time series, which exploits recent progress in our ability to summarize time series with dictionaries.
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces
Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignment task as a Wasserstein-Procrustes problem.
Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer.
Deep Learning on Attributed Sequences
Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length.
One-Shot Learning on Attributed Sequences
In this paper, we study the problem of one-shot learning on attributed sequences, where each instance is composed of a set of attributes (e. g., user profile) and a sequence of categorical items (e. g., clickstream).
Error-bounded Approximate Time Series Joins Using Compact Dictionary Representations of Time Series
The matrix profile is an effective data mining tool that provides similarity join functionality for time series data.
Embedding Compression with Hashing for Efficient Representation Learning in Graph
When applying such type of networks on graph without node feature, one can extract simple graph-based node features (e. g., number of degrees) or learn the input node representation (i. e., embeddings) when training the network.
Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks
In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database.
MLAS: Metric Learning on Attributed Sequences
Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback.
Merchant Category Identification Using Credit Card Transactions
In this work, we approach this problem from a multi-modal learning perspective, where we use not only the merchant time series data but also the information of merchant-merchant relationship (i. e., affinity) to verify the self-reported business type (i. e., merchant category) of a given merchant.
Towards a Flexible Embedding Learning Framework
Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database.
Multi-stream RNN for Merchant Transaction Prediction
New challenges have surfaced in monitoring and guaranteeing the integrity of payment processing systems.
Multi-future Merchant Transaction Prediction
We use experiments on real-world merchant transaction data to demonstrate the effectiveness of our proposed model.
Attributed Sequence Embedding
This problem is core to many important data mining tasks ranging from user behavior analysis to the clustering of gene sequences.
Constrained Non-Affine Alignment of Embeddings
Embeddings are already essential tools for large language models and image analysis, and their use is being extended to many other research domains.
