The concept of injecting global information can be applied in various contexts outside of forecasting:
Natural Language Processing (NLP): In sentiment analysis tasks, injecting global sentiment information across different sentences or documents could enhance understanding at a broader level without losing details within individual texts.
Image Processing: In object detection applications, injecting contextual information about objects' relationships within an image could improve accuracy by considering holistic scene understanding alongside local features.
Healthcare Monitoring Systems: Applying global information injection techniques in medical data analysis could help identify patterns across diverse patient parameters for better disease diagnosis and treatment planning.
Financial Analysis: Utilizing injected global financial market trends into individual stock price predictions may provide a comprehensive view for investors looking at multiple stocks simultaneously while focusing on specific company characteristics.
By adapting InjectTST's methodology creatively across different fields, researchers can potentially unlock new insights and advancements through enhanced modeling capabilities with selective incorporation of valuable global context into localized data representations.