8 papers · 1 filter
Perseus: Interactive Time Series Segmentation with Sparse Supervision via Stateful Memory
Ching Chang, Ming-Chih Lo, Chiao-Tung Chan +2
Real-world systems, ranging from industrial manufacturing to wearable healthcare, generate multivariate time series with hierarchical states ranging from coarse regimes to fine-gra…
PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation
Ching Chang, Ming-Chih Lo, Wen-Chih Peng +1
Multivariate time series data, collected across various fields such as manufacturing and wearable technology, exhibit states at multiple levels of granularity, from coarse-grained…
LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
Ching Chang, Wei-Yao Wang, Wen-Chih Peng +1
Multivariate time-series forecasting is vital in various domains, e.g., economic planning and weather prediction. Deep train-from-scratch models have exhibited effective performanc…
Text2Freq: Learning Series Patterns from Text via Frequency Domain
Ming-Chih Lo, Ching Chang, Wen-Chih Peng
Traditional time series forecasting models mainly rely on historical numeric values to predict future outcomes.While these models have shown promising results, they often overlook…
Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series
Ching Chang, Chiao-Tung Chan, Wei-Yao Wang +2
Multivariate time-series data in fields like healthcare and industry are informative but challenging due to high dimensionality and lack of labels. Recent self-supervised learning…
COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data
Ting-Yun Ou, Ching Chang, Wen-Chih Peng
Understanding causal relationships between machines is crucial for fault diagnosis and optimization in manufacturing processes. Real-world datasets frequently exhibit up to 90% mis…