Fidel-TS creates a new benchmark for time series forecasting models.
problem Lack of high-quality benchmarks for time series forecasting models.
method Formalized high-fidelity benchmark principles, including data sourcing integrity, leak-free design, and structural clarity. Created Fidel-TS, a new large-scale benchmark.
result Demonstrated the limitations of prior benchmarks and potential discrepancies in model evaluation.
MAESTRO improves multimodal learning for dynamic time series with adaptive attention and robustness.
problem Challenges in multimodal learning, especially in healthcare and daily living.
method Dynamic intra- and cross-modal interactions, symbolic tokenization, adaptive attention budgeting, sparse cross-modal attention, MoE mechanism.
result Average relative improvements of 4% and 8% over existing multimodal and multivariate approaches, respectively, under complete observations.
FinZero improves financial time series forecasting accuracy with multimodal modeling.
problem Lack of interpretability, uncertainty, and scalability in financial time series forecasting.
method Developed a multimodal pre-trained model FinZero using UARPO method for reasoning, prediction, and uncertainty analysis.
result FinZero achieves an approximate 13.48% improvement in prediction accuracy over GPT-4o in high-confidence group.
Enhances stock movement prediction using Higher Order Transformers for multimodal time-series data.
problem Predicting stock movements in financial markets with complex dynamics.
method Introduced Higher Order Transformers, extending self-attention and transformer architecture to capture complex market dynamics. Employed low-rank tensor decomposition and kernel attention to manage computational complexity. Integrated technical and fundamental analysis from historical prices and tweets.
result Demonstrated effectiveness of the method on the Stocknet dataset, improving stock movement prediction.
Integrating deep learning with latent state space models has the potential to yield temporal models that are powerful, yet tractable and interpretable. Unfortunately, current models are not designed to handle missing data or multiple data modalities, which are both prevalent in real-world data. In this work, we introdu…
There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, naturally occurring multimodal data is often imperfect as a result of imperfect modalities, missing entries or noise corruption. To address these c…
Framework detects anomalies in industrial processes using deep learning.
problem Detect anomalies in complex industrial processes.
method Causal-based framework with unsupervised deep learning.
result Successfully validated abstract contexts of blast furnace assets.
Unified model integrates text and time series for financial forecasting.
problem Challenges in integrating complementary modalities for improved forecasting.
method Modality-specific experts and cross-modal alignment framework.
result State-of-the-art performance on financial forecasting task.
This study improves stock price prediction using multimodal data.
problem Improving financial asset price forecasting accuracy.
method Combining candlestick time series and textual news flow data using LSTM and pre-trained models.
result Textual modality reduces MAPE by 55%.
TAMA uses LMMs to detect and interpret anomalies in time series data with few labels.
problem Challenges in manual feature engineering and extensive labeled training data for TSAD.
method Leverages LMMs to convert time series into visual formats for few-shot in-context learning.
result Consistently outperforms state-of-the-art methods in TSAD tasks.
Unified framework for generating meteorological time series from text.
problem Lack of large-scale, physically grounded multimodal datasets and architectures ignoring spectral-temporal structure.
method Introduce MeteoCap-3B dataset and MTransformer model.
result State-of-the-art generation quality, accurate cross-modal alignment, strong semantic controllability.
RiskLabs uses LLMs to predict financial risks from multimodal data.
problem Financial risk prediction using AI techniques.
method Integrates multimodal financial data (textual, vocal, time series, news) into LLMs for prediction.
result Empirical results show effectiveness in forecasting market volatility and variance.
This paper improves stock price prediction using multimodal data.
problem Accurate stock price prediction with diverse data integration.
method Combining financial metrics, tweets, and news articles through multimodal machine learning.
result Significant performance improvement in stock price prediction by up to 5%.
Survey on LLMs for time series analytics across various domains.
problem Cross-modality gap between LLMs and time series data.
method Taxonomy of approaches, cross-modality strategies, and experiments on multimodal datasets.
result Effective combinations of textual data and cross-modality strategies enhance time series analytics.
Models predict emotional valence from narratives, matching human raters.
problem Predicting emotional valence from multimodal time-series data.
method Adapted attention-based mechanisms (Transformer, Memory Fusion Network) to emotional narratives.
result Models perform well, matching human raters on emotional valence prediction.
Open-FinLLMs tackle financial tasks with multimodal capabilities.
problem Financial LLMs lack multimodal capabilities and real-world applicability.
method Developed Open-FinLLMs, an open-source multimodal financial LLM suite.
result Open-FinLLMs outperform advanced financial and general LLMs in diverse tasks.
BiN normalizes financial time-series for better forecasting.
problem Non-stationarity and multimodality in financial time-series data.
method Bilinear Normalization (BiN) incorporated into TABL networks.
result BiN-TABL outperforms other normalization methods in financial forecasting.
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstra…
Model predicts Bitcoin's future movements using multimodal pattern matching.
problem Challenges in predicting Bitcoin's volatile future movements.
method Ranking similar past chart patterns given current chart information.
result Improves directional prediction of Bitcoin's future movements.
MSIN model discovers relevant financial news for time series data.
problem Discovering relevant textual stories associated with numerical time series data.
method Joint learning of time series and text data using MSIN model.
result MSIN achieves up to 84.9% and 87.2% in recalling ground truth articles for two stock time series.
EMDLOT predicts bond defaults better than traditional methods.
problem Lack of interpretability and irregular temporal dependencies in financial data.
method Integrates time-series and textual data, uses Time-Aware LSTM, soft clustering, and multi-level attention.
result EMDLOT outperforms traditional and deep learning benchmarks in recall, F1-score, and mAP.
Unified model predicts stock and systemic risks from diverse financial data.
problem Isolating financial tasks leads to missed cross-scale dependencies.
method Shared Transformer backbone with modular task heads for cross-modal attention and multi-task optimization.
result Uni-FinLLM significantly outperforms baselines in stock forecasting, credit-risk assessment, and systemic-risk detection.
Proposes a method to apply conformal prediction to probabilistic time series forecasting models.
problem Obtaining accurate prediction regions for multi-step time series forecasting with probabilistic models.
method Conformalises conditional normalising flows to generate potentially disjoint prediction regions.
result Improves predictive efficiency in time series forecasting with multimodal distributions.
Novel SVAE learns interpretable discrete data representations from deep learning.
problem Learning interpretable discrete data representations from deep learning.
method Structured variational autoencoder (SVAE) with novel optimization algorithms.
result First competitive comparisons with state-of-the-art time series models.
Paper tackles imbalanced time series classification with a novel oversampling method.
problem Imbalanced time series classification challenges due to high dimensionality and correlation.
method Density-ratio based clustering followed by shrinkage technique for covariance estimation, then generating synthetic samples.
result OHIT outperforms state-of-the-art methods in F1, G-mean, and AUC metrics.
DAM improves cryptocurrency trend forecasting using multimodal data.
problem Simplistic merging of sentiment data in cryptocurrency trend forecasting.
method Dual Attention Mechanism (DAM) integrating financial metrics and sentiment analysis.
result DAM outperforms conventional models by up to 20% in prediction accuracy.
DMSTF models spatio-temporal data with deep Markov priors.
problem Analyzing nonlinear multimodal spatio-temporal dynamics.
method Deep Markov spatio-temporal factorization with stochastic variational inference.
result DMSTF outperforms other methods in predictive performance and clustering.
Deep Learning (DL) models can be used to tackle time series analysis tasks with great success. However, the performance of DL models can degenerate rapidly if the data are not appropriately normalized. This issue is even more apparent when DL is used for financial time series forecasting tasks, where the non-stationary…
Human Activity Recognition (HAR) based on motion sensors has drawn a lot of attention over the last few years, since perceiving the human status enables context-aware applications to adapt their services on users' needs. However, motion sensor fusion and feature extraction have not reached their full potentials, remain…
Late fusion of clinical notes and physiological data improves ICU mortality prediction.
problem Improving ICU mortality prediction using multimodal data.
method Late fusion of clinical notes and physiological time series data with a deep learning architecture.
result Late fusion approach provides statistically significant improvement in mortality prediction performance.
Benchmark assesses forecasting models' ability to use textual context.
problem Forecasting models struggle with integrating textual context.
method Introduces a benchmark with numerical and textual data, evaluates various models.
result LLM prompting method outperforms other models.
Paper proposes a method to preserve multimodal sentiment analysis fidelity.
problem Lack of fidelity in multimodal fusion for sentiment analysis.
method Variational autoencoder-based approach for modality fusion.
result Empirically shows superior performance over state-of-the-art methods.
Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of j…
TCT learns multimodal sequence representations by translating from related sequences.
problem Challenges in learning semantic representations from multimodalities.
method Transformer based Cross-modal Translator (TCT) combined with Multimodal Transformer Network (MTN).
result Proposed method achieves new state-of-the-art performance on video-grounded dialogue.
Multimodal bitransformer boosts image-text classification.
problem Combining text and image modalities for improved classification.
method Supervised multimodal bitransformer model integrating text and image encoders.
result State-of-the-art performance on multimodal classification benchmarks.
Survey of multimodal deep generative models for diverse data types.
problem Inference of shared representations and cross-modal generation from heterogeneous multimodal data.
method Variational autoencoders and other deep generative models.
result A comprehensive survey of multimodal deep generative models.
FMT model improves multimodal sequential learning across language, vision, and acoustic data.
problem Modeling spatio-temporal dynamics across multiple modalities.
method Factorized Multimodal Transformer (FMT) that models intramodal and intermodal dynamics in a factorized manner.
result FMT outperforms existing models on 3 datasets and 21 labels, setting new state of the art.
Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact multimodal representation. Previous research in this field has exp…
Develops a contrastive framework for data-efficient multimodal learning.
problem Expensive training of multimodal generative models requiring related multimodal data.
method Contrastive framework for multimodal learning, distinguishing related from unrelated data.
result Data-efficient multimodal learning on challenging datasets for various VAE models.
Plug-and-play multimodal controller improves class-conditional image generation.
problem Generating class-conditional images from user-specified labels.
method Introduces a `multimodal controller` to generate multimodal data without additional learning parameters.
result Multimodal controlled generative models produce higher quality class-conditional images and novel modalities.
StockTime predicts stock prices more accurately using LLMs and time series data.
problem Challenges in integrating time series data and natural language for stock price prediction.
method StockTime is a specialized LLM architecture that integrates textual and time series data to predict stock prices.
result StockTime outperforms recent LLMs in predicting stock prices with more accuracy.
Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data: 1) mode…
IQ-BART models conditional quantiles using a non-parametric Bayesian approach.
problem Capturing multimodal predictive distributions in time series forecasting.
method Implicit Quantile BART (IQ-BART) augments data with quantile values for non-parametric quantile function estimation.
result IQ-BART provides flexible distribution-free regression with theoretical guarantees.
Improved sentiment analysis with multimodal data.
problem Cross-modal sentiment analysis in social media, customer service, and video blogs.
method Gated mechanism for attention-based learning of cross-modal interactions, with experiments on CMU-MOSI and CMU-MOSEI datasets.
result 1.6% and 1.34% absolute improvement over state-of-the-art.
Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent r…
Paper proposes multimodal contrastive learning for EHR data.
problem Separate treatment of structured and unstructured EHR data.
method Proposes a multimodal feature embedding generative model and a multimodal contrastive loss.
result Multimodal learning yields better feature representation than single-modality learning.
Combines deep state space models with diffusion models for better forecasting and capturing latent dynamics
problem Forecasting and capturing latent dynamics in time series
method DDSSM: Diffusion-driven state space model
result Empirically outperforms state-of-the-art deep SSM
Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the in…