ENTERPRISE AI ANALYSIS
Optimizing the Training Diet: Data Mixture Search for Robust Time Series Forecasting
This paper introduces a novel data-centric optimization framework for time series forecasting, moving beyond the 'more data is always better' paradigm. By leveraging pre-trained encoders, clustering, and Optuna-based optimization, we identify optimal training data mixtures. Our method significantly improves model performance and generalization (19.41% MSE reduction on PMSM dataset) with less data (42.6% of original), proving that curated data diets are superior to raw, unoptimized datasets.
Executive Impact: Key Metrics
Our innovative approach to time series data optimization yields significant performance improvements and efficiency gains, crucial for enterprise applications dealing with vast sensor data streams. By reducing the data volume while enhancing model accuracy, businesses can achieve faster training, lower computational costs, and more reliable predictive models, leading to better operational decisions and resource allocation.
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Performance Improvement (MSE)
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Data Volume Reduction
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Model Generalization Uplift
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Training Efficiency
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Methodology
Performance
Interpretability
Methodology
Our framework involves embedding raw time series data into a unified representation space using large pre-trained encoders, partitioning this space into distinct operational regimes via K-Means clustering, and then optimizing the composition of these regimes using Optuna to maximize downstream model performance. This data-centric approach directly tunes the training diet.
Performance
Experiments on the PMSM dataset show that our optimized data mixture consistently outperforms baselines trained on the full dataset. It achieves a 19.41% improvement in average MSE (from 1.70 to 1.37) while using only 42.6% of the original data, demonstrating superior prediction accuracy and efficiency.
Interpretability
We utilized an LLM-as-a-reviewer step to qualitatively analyze cluster-specific behaviors. This revealed that highly weighted clusters contain rich, structured variations indicative of fundamental system operations, while low-weighted clusters often contain uninformative patterns like flatlines or simple noise, justifying their pruning.
Enterprise Process Flow
Raw Time Series Data
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Large Encoder (MOMENT-1)
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K-Means Clustering
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Optuna Optimization
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Data Mixture Sampling
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Target Model Training & Evaluation
19.41%
Improvement in Average MSE (PMSM Dataset)
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PMSM Dataset: From Redundancy to Precision
On the PMSM dataset, our framework demonstrated a significant breakthrough. By intelligently selecting only 42.6% of the original raw sensor data, we achieved an average MSE of 1.37, a 19.41% improvement over the baseline model trained on the entire dataset (MSE 1.70). This result highlights that quality over quantity in data selection leads to more accurate and robust time series forecasting models, even in complex industrial applications.
42.6%
Of Original Data Used for Superior Performance
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Your Implementation Roadmap
Our structured approach ensures a seamless integration and rapid value realization for your enterprise AI initiatives.
Phase 1: Data Assessment & Embedding
Evaluate existing time series data sources and integrate with our pre-trained foundational encoder (MOMENT-1) to create rich, task-agnostic embeddings that capture temporal dynamics.
Phase 2: Behavioral Clustering
Apply K-Means clustering to partition the embedded data into distinct, behaviorally consistent clusters, representing key operational regimes or patterns within your data.
Phase 3: Training Diet Optimization
Utilize Optuna-based search to discover the optimal sampling ratios for each data cluster. This iteratively refines the training data composition to maximize target model performance on your specific downstream task.
Phase 4: Model Deployment & Monitoring
Train your target forecasting model on the optimized data mixture. Deploy the robust, high-performing model and integrate continuous monitoring to ensure sustained accuracy and adaptability to new data.
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