The field of time series forecasting and analysis is rapidly evolving, with a focus on developing innovative models and techniques to improve prediction accuracy and efficiency. Recent research has explored the integration of various data modalities, such as textual and numerical data, to enhance forecasting performance. Additionally, there is a growing interest in leveraging techniques from other domains, like computer vision and natural language processing, to improve time series analysis. Notably, the use of large language models and transformer-based architectures has shown promising results in capturing complex patterns and relationships in time series data. Furthermore, researchers are also investigating the application of time series forecasting in diverse domains, including finance, healthcare, and environmental monitoring. Overall, the field is moving towards developing more robust, adaptable, and interpretable models that can handle complex and high-dimensional data. Noteworthy papers include the proposal of DMSC, a dynamic multi-scale coordination framework for time series forecasting, and VisionTS++, a cross-modal time series foundation model that performs continual pre-training on large-scale time series datasets. These advancements have the potential to significantly impact various fields and applications, enabling more accurate and informed decision-making.
Advancements in Time Series Forecasting and Analysis
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Evaluating COVID 19 Feature Contributions to Bitcoin Return Forecasting: Methodology Based on LightGBM and Genetic Optimization
KFS: KAN based adaptive Frequency Selection learning architecture for long term time series forecasting
Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models
DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting
CauKer: classification time series foundation models can be pretrained on synthetic data only
Minimal Convolutional RNNs Accelerate Spatiotemporal Learning
Prediction-Oriented Subsampling from Data Streams
Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
DP-GPT4MTS: Dual-Prompt Large Language Model for Textual-Numerical Time Series Forecasting
T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion
VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Visual Backbones
PRISM: Lightweight Multivariate Time-Series Classification through Symmetric Multi-Resolution Convolutional Layers
InceptoFormer: A Multi-Signal Neural Framework for Parkinson's Disease Severity Evaluation from Gait
PA-RNet: Perturbation-Aware Reasoning Network for Multimodal Time Series Forecasting
Retrieval-Augmented Water Level Forecasting for Everglades
Sentiment-Aware Stock Price Prediction with Transformer and LLM-Generated Formulaic Alpha
Marine Chlorophyll Prediction and Driver Analysis based on LSTM-RF Hybrid Models
FlowState: Sampling Rate Invariant Time Series Forecasting
Echo State Networks for Bitcoin Time Series Prediction