Moirai 1.1 R Base
A major upgrade of the Moirai-1.0-R model, demonstrating significant improvements across 40 datasets from the Monash repository, with special optimizations for low-frequency data scenarios.
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Release Time : 6/14/2024
Model Overview
A foundational time series forecasting model focused on improving prediction accuracy for low-frequency data (e.g., annual/quarterly data).
Model Features
Low-frequency data optimization
Specially optimized for annual/quarterly low-frequency data scenarios, achieving ~20% improvement in Normalized Mean Absolute Error (NMAE)
Extensive dataset validation
Performance validated across 40 datasets from the Monash repository
Academic research oriented
Designed specifically for academic research with strict ethical usage restrictions
Model Capabilities
Time series forecasting
Low-frequency data analysis
Cross-domain time series modeling
Use Cases
Economic forecasting
Annual economic indicator prediction
Forecasting annual economic indicators like GDP and inflation rates
Excellent performance in low-frequency data scenarios
Business analytics
Quarterly sales forecasting
Predicting corporate quarterly sales data
Ideal for low-frequency business data analysis scenarios
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