Enterprise AI Analysis
Unlocking Robustness: Contrastive Time Series Forecasting with Anomalies
This paper introduces Co-TSFA, a novel contrastive regularization framework for time-series forecasting under anomalous conditions. It addresses the critical challenge of distinguishing between forecast-relevant (persistent shifts) and forecast-irrelevant (short-lived noise) anomalies. By employing input-only and input-output augmentations and a latent-output alignment loss, Co-TSFA learns to ignore noise while adapting to meaningful shifts. Experimental results on Traffic, Electricity, and a real-world Cash Demand dataset demonstrate Co-TSFA's superior performance in anomalous scenarios without compromising accuracy on normal data. A key finding is a 21.1% reduction in MSE for iTransformer under severe Input+Output anomalies, showcasing its robust adaptability.
Quantifiable Impact on Your Enterprise
Co-TSFA revolutionizes time-series forecasting by intelligently distinguishing between transient noise and critical, persistent anomalies. This adaptive approach ensures your operational forecasts remain stable and accurate, even in volatile real-world conditions.
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MSE Reduction (Input+Output anomalies)
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Improved Accuracy (Normal Data)
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Adaptive Response to Regime Shifts
Deep Analysis & Enterprise Applications
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Core Innovation
Anomaly Handling
Real-World Application
Core Innovation: Latent-Output Alignment
Co-TSFA introduces a novel latent-output alignment loss that ties representation changes to forecast changes. This framework specifically penalizes discrepancies between the similarity of latent representations and the similarity of their associated outputs under augmented scenarios. This encourages invariance to irrelevant perturbations while preserving sensitivity to meaningful distributional shifts, a critical balance for robust forecasting.
Anomaly Handling: Differentiating Noise from Shifts
The framework addresses two key types of anomalies through targeted augmentations: input-only anomalies (corrupted history that should be ignored) and input-output anomalies (persistent shifts requiring forecast adaptation). This explicit distinction allows Co-TSFA to learn when to suppress variations and when to respond adaptively, enabling consistent performance across diverse real-world conditions.
Real-World Application: Cash Demand Forecasting
Co-TSFA demonstrates its practical utility on a real-world cash-demand dataset from ATMs, which exhibits high volatility and non-stationary behavior. Unlike standard benchmarks, this dataset presents irregular spikes and inconsistent fluctuation patterns (Figure 6). Co-TSFA improves forecasting accuracy under these challenging conditions, crucial for critical applications like ATM replenishment.
21.1%
MSE Reduction for iTransformer under Input+Output Anomalies (Table 1)
Co-TSFA achieved a significant 21.1% reduction in Mean Squared Error (MSE) for iTransformer, a leading forecasting model, when faced with challenging input-output anomalies. This highlights its capability to drastically improve performance in scenarios where anomalies persist into the prediction horizon, a critical limitation for traditional methods.
Enterprise Process Flow: Co-TSFA Adaptive Forecasting
Original Input (x, y)
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Generate Augmented Pairs (x', y')
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Encode Inputs (z = g(x), z' = g(x'))
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Compute Latent Similarity (sim(z,z'))
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Compute Output Similarity (sim(y,y'))
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Minimize Latent-Output Alignment Loss (L_align)
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Optimize Forecasting Loss (L_forecast)
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Robust & Adaptive Forecast (ŷ)
| Anomaly Type | RobustTSF (MAE) | Co-TSFA (MAE) | Co-TSFA Advantage |
|---|---|---|---|
| Clean | 0.1927 | 0.1545 | ✓ Significant |
| Continuous (Input-Only) | 0.5135 | 0.1862 | ✓✓ Substantial |
| Continuous (Input+Output) | 0.8647 | 0.2064 | ✓✓ Critical |
| Pointwise (Const, 30%) | 0.3448 | 0.3039 | ✓ Improved |
| Pointwise (Missing, 30%) | 0.6045 | 0.4814 | ✓ Improved |
| Pointwise (Gaussian, 30%) | 0.7872 | 1.0647 | Slight Degradation (MAE), but often better MSE |
Co-TSFA consistently outperforms RobustTSF, particularly under continuous anomalies (Input-Only and Input+Output), as shown in Table 2. While RobustTSF's MAE can escalate to 0.8647 under Input+Output, Co-TSFA maintains a significantly lower MAE of 0.2064, demonstrating superior robustness and adaptive capabilities. Even for some pointwise anomalies at higher ratios (e.g., Gaussian 30%), Co-TSFA might show a slight MAE increase, but often achieves better MSE.
Case Study: Enhancing Cash Demand Forecasting for ATMs
The Problem: Traditional forecasting models struggle with the highly irregular and non-stationary patterns observed in real-world cash demand data from ATMs. This includes abrupt spikes, high volatility, and inconsistent seasonal patterns, making reliable predictions challenging.
Co-TSFA's Solution: Co-TSFA's ability to differentiate between transient noise and persistent shifts makes it uniquely suited for this environment. By learning from targeted augmentations, it adapts to meaningful demand shifts while ignoring short-lived fluctuations.
Impact & Results: Implementation of Co-TSFA on the Cash Demand dataset (Table 1) resulted in a 4.6% reduction in MAE and 8.6% reduction in SMAPE for Autoformer under Input+Output anomalies, significantly improving forecast accuracy and operational efficiency for ATM replenishment.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your Co-TSFA Implementation Roadmap
Our phased approach ensures a seamless integration of Co-TSFA into your existing time-series infrastructure, maximizing impact with minimal disruption.
Phase 1: Discovery & Strategy
Detailed assessment of your current forecasting workflows, data sources, and business objectives. We identify key anomalous scenarios and tailor a Co-TSFA implementation strategy.
Phase 2: Data Engineering & Model Training
Preparation of historical data, development of custom anomaly augmentation pipelines, and training of Co-TSFA models on your specific datasets, leveraging powerful backbones.
Phase 3: Integration & Validation
Seamless integration of the robust Co-TSFA forecasting engine into your existing systems. Rigorous validation against real-world anomalous data to ensure adaptive and accurate predictions.
Phase 4: Monitoring & Optimization
Continuous monitoring of model performance under evolving conditions, with ongoing refinement and optimization to maintain peak forecasting accuracy and robustness.
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