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Domain Adaptation for Industrial Time-series Forecasting via Counterfactual Inference

Chao Min, Guoquan Wen, Jiangru Yuan, Jun Yi, Xing Guo

TL;DR

The paper tackles data scarcity and policy-induced domain shifts in industrial time-series forecasting by introducing a Causal Domain Adaptation (CDA) forecaster that merges causal inference with domain adaptation. It develops a joint Treatment-Outcomes model guided by an answer-based attention mechanism grounded in position-wise CATE to align source and target domains, and it optimizes a minimax objective with CMMD-based domain alignment. Key contributions include a shared-causality module for treatment and outcome terms, counterfactual estimation for policy guidance, and an adversarial training framework that yields treatment-invariant representations with practical oilfield applications. The approach is validated on real-world and synthetic oilfield datasets, showing improvements over single-domain and other cross-domain baselines and providing actionable policy insights for production optimization.

Abstract

Industrial time-series, as a structural data responds to production process information, can be utilized to perform data-driven decision-making for effective monitoring of industrial production process. However, there are some challenges for time-series forecasting in industry, e.g., predicting few-shot caused by data shortage, and decision-confusing caused by unknown treatment policy. To cope with the problems, we propose a novel causal domain adaptation framework, Causal Domain Adaptation (CDA) forecaster to improve the performance on the interested domain with limited data (target). Firstly, we analyze the causality existing along with treatments, and thus ensure the shared causality over time. Subsequently, we propose an answer-based attention mechanism to achieve domain-invariant representation by the shared causality in both domains. Then, a novel domain-adaptation is built to model treatments and outcomes jointly training on source and target domain. The main insights are that our designed answer-based attention mechanism allows the target domain to leverage the existed causality in source time-series even with different treatments, and our forecaster can predict the counterfactual outcome of industrial time-series, meaning a guidance in production process. Compared with commonly baselines, our method on real-world and synthetic oilfield datasets demonstrates the effectiveness in across-domain prediction and the practicality in guiding production process

Domain Adaptation for Industrial Time-series Forecasting via Counterfactual Inference

TL;DR

The paper tackles data scarcity and policy-induced domain shifts in industrial time-series forecasting by introducing a Causal Domain Adaptation (CDA) forecaster that merges causal inference with domain adaptation. It develops a joint Treatment-Outcomes model guided by an answer-based attention mechanism grounded in position-wise CATE to align source and target domains, and it optimizes a minimax objective with CMMD-based domain alignment. Key contributions include a shared-causality module for treatment and outcome terms, counterfactual estimation for policy guidance, and an adversarial training framework that yields treatment-invariant representations with practical oilfield applications. The approach is validated on real-world and synthetic oilfield datasets, showing improvements over single-domain and other cross-domain baselines and providing actionable policy insights for production optimization.

Abstract

Industrial time-series, as a structural data responds to production process information, can be utilized to perform data-driven decision-making for effective monitoring of industrial production process. However, there are some challenges for time-series forecasting in industry, e.g., predicting few-shot caused by data shortage, and decision-confusing caused by unknown treatment policy. To cope with the problems, we propose a novel causal domain adaptation framework, Causal Domain Adaptation (CDA) forecaster to improve the performance on the interested domain with limited data (target). Firstly, we analyze the causality existing along with treatments, and thus ensure the shared causality over time. Subsequently, we propose an answer-based attention mechanism to achieve domain-invariant representation by the shared causality in both domains. Then, a novel domain-adaptation is built to model treatments and outcomes jointly training on source and target domain. The main insights are that our designed answer-based attention mechanism allows the target domain to leverage the existed causality in source time-series even with different treatments, and our forecaster can predict the counterfactual outcome of industrial time-series, meaning a guidance in production process. Compared with commonly baselines, our method on real-world and synthetic oilfield datasets demonstrates the effectiveness in across-domain prediction and the practicality in guiding production process
Paper Structure (14 sections, 4 theorems, 31 equations, 7 figures, 4 tables)

This paper contains 14 sections, 4 theorems, 31 equations, 7 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Casual Inference
  4. Domain Adaptation under Causality
  5. Method
  6. Problem formulation
  7. Joint Treatment-Outcome Model
  8. The Causal Domain Adaptation
  9. Experimental Verification
  10. Experimental Apparatus
  11. Inside-well Time-series Forecasting
  12. Cross-well Time-sereis Predicting
  13. Optimal Policy Determining
  14. Conclusion

Key Result

Theorem 1

Let $\mu[P]$ be a distribution of $P$ in RKHS $\mathcal{H}_{k}$, then via the reproducing property of RKHS $\mathcal{H}_{k}$, we have $\langle\phi, \mu[P_{\mathcal{T},\mathcal{S}}]\rangle=\mathbb{E}_{\mathbf{X}\sim\mathcal{T},\mathcal{S}}[\phi(\mathbf{X})]$ for MMD, $\langle\phi, \mu[P_{\mathcal{T}, where $d_{\mathrm{MMD}}$ is the standard Maximum Mean Distance (MMD), $d_{\mathrm{CMMD}}(\mathcal{S

Figures (7)

  • Figure 1: The causal graph $\mathcal{G}$ in continuous time, where $a\rightarrow b$ means $a$ is the direct cause of $b$. And the treatment policy $z_{t+1}$ can be specified by human (dashed arrow) or determined by previous outcome $\mathbf{Y}_{t}$, $\mathbf{U}$ denote the static variables.
  • Figure 2: The shared causality for both both domains in treatment term and outcome term. (a). The causality in treatment-policy term; (b). The causality in outcome term.
  • Figure 3: An architecture overview of CDA forecaster
  • Figure 4: The location of oil wells.
  • Figure 5: Performance comparison of CFDA, best single-domain forecaster and cross-domain forecaster in estimating treatment-outcome
  • ...and 2 more figures

Theorems & Definitions (11)

  • Definition 1: Position-wise CATE
  • Definition 2: Conditional Markov Model for Sequence Outcomes
  • Definition 3: Conditional Maximum Mean Discrepancy
  • Theorem 1
  • Remark 1
  • Corollary 1
  • Remark 2
  • Theorem 1
  • proof
  • Corollary 1
  • ...and 1 more