View RSS Feed

newdigital

Neural Networks Made Easy (Part 87): Time Series Patching

Rate this Entry
by , 11-12-2024 at 06:34 AM (61 Views)
      
   
Forecasting plays an important role in time series analysis. Deep models have brought significant improvement in this area. In addition to successfully predicting future values, they also extract abstract representations that can be applied to other tasks such as classification and anomaly detection.
The Transformer architecture, which originated in the field of natural language processing (NLP), demonstrated its advantages in computer vision (CV) and is successfully applied in time series analysis. Its Self-Attention mechanism, which can automatically identify relationships between elements of a time series, has become the basis for creating effective forecasting models.
more...

Submit "Neural Networks Made Easy (Part 87): Time Series Patching" to Google Submit "Neural Networks Made Easy (Part 87): Time Series Patching" to del.icio.us Submit "Neural Networks Made Easy (Part 87): Time Series Patching" to Digg Submit "Neural Networks Made Easy (Part 87): Time Series Patching" to reddit

Tags: None Add / Edit Tags
Categories
Uncategorized

Comments