Finding order in chaos: A novel data augmentation method for time series in contrastive learning
BU Demirel, C Holz - Advances in Neural Information …, 2023 - proceedings.neurips.cc
The success of contrastive learning is well known to be dependent on data augmentation.
Although the degree of data augmentations has been well controlled by utilizing pre-defined …
Although the degree of data augmentations has been well controlled by utilizing pre-defined …
Decomposition-based Data Augmentation for Time-series Building Load Data
Building load data, ie, building electricity demands, are important for many downstream
applications such as load forecasting, demand response, and others. Recent applications, in …
applications such as load forecasting, demand response, and others. Recent applications, in …
D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting
X Yuan, L Chen - arxiv preprint arxiv:2403.17814, 2024 - arxiv.org
In time series forecasting, effectively disentangling intricate temporal patterns is crucial.
While recent works endeavor to combine decomposition techniques with deep learning …
While recent works endeavor to combine decomposition techniques with deep learning …
Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction
Recent studies have suggested frequency-domain Data augmentation (DA) is effec tive for
time series prediction. Existing frequency-domain augmentations disturb the original data …
time series prediction. Existing frequency-domain augmentations disturb the original data …
Unifying Physics and Semantics for Robust Sensor Time Series Analysis
X Zhang - 2024 - search.proquest.com
The past decade has witnessed a significant growth of deployed sensors in our daily life,
covering applications from healthcare, climate modeling to home automation and robotics …
covering applications from healthcare, climate modeling to home automation and robotics …
[引用][C] Challenges in Deep Learning Based Forecasting of Time Series with Calendar-driven Periodicities
BM Heidrich - 2024