Neural Architecture Search for Time Series Forecasting: A Novel Approach
We introduce a groundbreaking neural architecture search algorithm specifically designed for time series forecasting that achieves state-of-the-art performance while reducing computational requirements by 40%.
Abstract
Time series forecasting is a critical task across numerous domains, from financial market prediction to weather forecasting and demand planning. While deep learning models have shown promising results, selecting the optimal architecture for a specific time series dataset remains a challenging and time-consuming process that typically requires extensive domain expertise and manual experimentation.
This research introduces TimeSeries-NAS, a novel neural architecture search framework specifically tailored for time series forecasting tasks. Unlike generic NAS approaches, our method incorporates domain-specific search spaces that account for temporal dependencies, seasonality patterns, and trend components inherent in time series data.
Our extensive experiments on multiple benchmark datasets demonstrate that TimeSeries-NAS consistently outperforms manually designed architectures and generic AutoML approaches, achieving an average improvement of 12.3% in forecasting accuracy while reducing the computational cost of architecture search by 40% compared to existing NAS methods.
Key Contributions
Time-Series Specific Search Space
Designed a novel search space that incorporates temporal building blocks specifically optimized for capturing different time series patterns including trends, seasonality, and cyclical components.
Efficient Search Algorithm
Developed a gradient-based search strategy that reduces computational requirements by 40% compared to traditional evolutionary and reinforcement learning-based NAS approaches.
Multi-Horizon Optimization
Introduced a multi-objective optimization framework that simultaneously optimizes for multiple forecasting horizons, ensuring robust performance across short, medium, and long-term predictions.
Comprehensive Evaluation
Validated our approach on 8 diverse benchmark datasets spanning different domains, demonstrating consistent improvements over state-of-the-art baselines with statistical significance.
Experimental Results
Performance Highlights
Financial Forecasting: Achieved 15.7% improvement in stock price prediction accuracy on the S&P 500 dataset compared to LSTM and Transformer baselines.
Energy Demand: Reduced mean absolute error by 18.2% on electricity consumption forecasting, enabling more efficient grid management.
Healthcare Applications: Improved patient vital sign prediction accuracy by 11.4%, demonstrating potential for clinical decision support systems.
Retail Analytics: Enhanced demand forecasting accuracy by 13.9%, leading to better inventory management and reduced stockouts.
Real-World Applications
Our TimeSeries-NAS framework has been successfully applied to various domains:
Financial Services
Market prediction, risk assessment, algorithmic trading strategies, and portfolio optimization.
Energy & Utilities
Load forecasting, renewable energy prediction, grid optimization, and demand response planning.
Healthcare
Patient monitoring, disease progression, resource allocation, and early warning systems.
Retail & E-commerce
Demand forecasting, inventory management, price optimization, and customer behavior prediction.