Current time series models often fail to fully utilize frequency characteristics.
The paper introduces FreqMoE, a novel time series forecasting model.
FreqMoE addresses limitations of existing methods by dynamically decomposing time series into frequency bands. Specialized experts process each band, improving forecasting accuracy and efficiency.
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📌 Dynamic Frequency Adaptation is Key
Fixed filters discard useful signal variations. FreqMoE adapts expert allocation dynamically, preserving crucial frequency details. This eliminates arbitrary frequency cutoffs and ensures each band is processed optimally, leading to sharper forecasts.
📌 Mixture of Experts Unlocks Specialized Learning
Traditional models struggle with multi-scale patterns. FreqMoE assigns expert networks to frequency bands, letting each expert specialize in distinct periodic structures. This specialization enhances generalization across diverse time series datasets.
📌 Efficient and Scalable with Minimal Parameters
FreqMoE outperforms larger models while using under 50,000 parameters. This efficiency comes from its modular expert design and adaptive frequency selection, proving that targeted learning in frequency space is superior to brute-force deep architectures.
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https://arxiv.org/abs/2501.15125
Methods explored in this Paper 😎:
→ It uses Frequency Decomposition Mixture of Experts (MoE).
→ First, time series input is transformed to the frequency domain using Fast Fourier Transform (FFT).
→ Frequency components are divided into bands.
→ Each band is processed by a specialized expert network.
→ A gating network dynamically assigns weights to each expert based on frequency magnitude.
→ Expert outputs are aggregated using these weights.
→ The aggregated output is fed into a prediction module with residual connections for iterative refinement.
→ This module uses stacked deep residual blocks for prediction in frequency domain.
→ Complex-valued linear layers in prediction blocks perform upsampling.
→ Inverse FFT transforms the frequency domain output back to the time domain for final forecast.
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Key Insights from this Paper 🤔:
→ Dynamically adjusting frequency band weights based on data characteristics is crucial.
→ Fixed filters can lead to loss of important frequency information.
→ Frequency Decomposition MoE module effectively captures intricate patterns in different frequency bands.
→ Gating mechanism allows adaptive weighting of experts, improving generalization.
→ Residual connections in prediction module refine forecasts by iteratively learning residual errors.
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Results 📊:
→ FreqMoE outperforms state-of-the-art models on 51 out of 70 metrics across datasets.
→ Achieves best performance on ETTh1 dataset with MSE of 0.440 and MAE of 0.429.
→ FreqMoE uses fewer than 50k parameters, demonstrating efficiency.
→ Ablation studies show Frequency Decomposition MoE module enhances performance.
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