Self‑Supervised Temporal Pattern Mining for Circular Manufacturing Supply Chains for Extreme Data Sparsity Scenarios

说明与用途

在循环供应链中，极度稀疏且不规则的退货数据导致传统预测方法失效。通过自监督对比学习，模型从未标记数据中提取可重复的时间模式，并在少量标记数据上微调，从而提升退货到达时间预测精度。

问题

Predicting return‑arrival times in a remanufacturing facility with fewer than 400 labeled events, irregular timestamps, and frequent regime shifts caused supervised models to overfit or produce flat predictions.

结果

The self‑supervised approach successfully extracted reusable temporal patterns from sparse, irregular data, improving prediction quality over traditional supervised methods in a circular manufacturing context.

工作流

• Collect unlabeled time‑series data (sensor readings, shipment timestamps, material logs).
• Apply TemporalAugmenter: magnitude scaling, temporal jitter, and random masking to create two views of each window.
• Train a dilated causal 1D convolutional encoder with contrastive loss to pull augmented views together and push apart different windows.
• Fine‑tune the encoder on the small labeled set to predict return‑arrival times.
• Deploy the fine‑tuned model to schedule labor and material flows in the remanufacturing facility.

证据

Traditional supervised forecasting collapsed. XGBoost overfit after a few dozen trees. LSTMs memorized the training set and produced flat, useless predictions on holdouts.

来源

The masking step was the single most impactful design choice. In sparse regimes, missing timestamps are the norm, not an anomaly, so training the encoder to reconstruct around gaps directly matches the deployment condition.

来源