arXiv:2606.20010v1 Announce Type: new Abstract: Current time series forecasting (TSF) research predominantly focuses on scale-homogeneous data, where different time series share similar numerical magnitude ranges. However, in real-world industrial scenarios such as financial product sales, different time series often differ by orders of magnitude (scale heterogeneity). Since these series share similar temporal patterns, joint modeling is desirable for better data utilization, yet existing scaling methods either compress low-scale signals (global normalization) or destroy semantic discriminabil
The continuous growth in scale-heterogeneous time series data across various industries necessitates advanced forecasting techniques capable of handling such complexity effectively.
Improved time series forecasting with scale heterogeneity has direct implications for sectors relying on diverse, real-world data, enhancing predictions in finance, supply chains, and industrial operations.
New methods for self-adaptive scale handling will lead to more accurate and robust forecasting models, particularly for datasets where different series vary significantly in magnitude.
- · Financial institutions
- · Logistics and supply chain companies
- · Industrial operations with diverse sensor data
- · AI/ML researchers in time series analysis
- · Companies relying on traditional, scale-homogeneous forecasting models
- · Legacy forecasting software vendors
More accurate predictions across heterogeneous datasets, leading to better operational decisions and resource allocation.
Increased adoption of specialized AI models for complex real-world data, fostering innovation in data science tools.
Potential for new predictive services and products born from the ability to reliably forecast previously intractable, disparate data streams.
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Read at arXiv cs.LG