Transferable Time-Series Forecasting under Causal Conditional Shift
- URL: http://arxiv.org/abs/2111.03422v3
- Date: Wed, 6 Sep 2023 07:29:31 GMT
- Title: Transferable Time-Series Forecasting under Causal Conditional Shift
- Authors: Zijian Li, Ruichu Cai, Tom Z.J Fu, Zhifeng Hao, Kun Zhang
- Abstract summary: We propose an end-to-end model for the semi-supervised domain adaptation problem on time-series forecasting.
Our method can not only discover the Granger-Causal structures among cross-domain data but also address the cross-domain time-series forecasting problem with accurate and interpretable predicted results.
- Score: 28.059991304278572
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper focuses on the problem of semi-supervised domain adaptation for
time-series forecasting, which is underexplored in literatures, despite being
often encountered in practice. Existing methods on time-series domain
adaptation mainly follow the paradigm designed for the static data, which
cannot handle domain-specific complex conditional dependencies raised by data
offset, time lags, and variant data distributions. In order to address these
challenges, we analyze variational conditional dependencies in time-series data
and find that the causal structures are usually stable among domains, and
further raise the causal conditional shift assumption. Enlightened by this
assumption, we consider the causal generation process for time-series data and
propose an end-to-end model for the semi-supervised domain adaptation problem
on time-series forecasting. Our method can not only discover the Granger-Causal
structures among cross-domain data but also address the cross-domain
time-series forecasting problem with accurate and interpretable predicted
results. We further theoretically analyze the superiority of the proposed
method, where the generalization error on the target domain is bounded by the
empirical risks and by the discrepancy between the causal structures from
different domains. Experimental results on both synthetic and real data
demonstrate the effectiveness of our method for the semi-supervised domain
adaptation method on time-series forecasting.
Related papers
- Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference [0.0]
We argue that, when evaluating a set of intervals, traditional measures alone are insufficient.
The intervals must vary in length, with this variation directly linked to the difficulty of the prediction.
We propose the Heteroscedastic Quantile Regression (HQR) model and the Width-Adaptive Conformal Inference ( WACI) method.
arXiv Detail & Related papers (2024-06-21T06:51:13Z) - PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
We propose a.
Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns.
We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
arXiv Detail & Related papers (2024-06-04T13:51:08Z) - Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift [9.387706860375461]
A distribution shift occurs when the underlying data-generating process changes, leading to a deviation in the model's performance.
The prediction interval serves as a crucial tool for characterizing uncertainties induced by their underlying distribution.
We propose methodologies for aggregating prediction intervals to obtain one with minimal width and adequate coverage on the target domain.
arXiv Detail & Related papers (2024-05-16T17:55:42Z) - When Rigidity Hurts: Soft Consistency Regularization for Probabilistic
Hierarchical Time Series Forecasting [69.30930115236228]
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting.
Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions.
We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
arXiv Detail & Related papers (2023-10-17T20:30:16Z) - Performative Time-Series Forecasting [71.18553214204978]
We formalize performative time-series forecasting (PeTS) from a machine-learning perspective.
We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts.
We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
arXiv Detail & Related papers (2023-10-09T18:34:29Z) - Variational Counterfactual Prediction under Runtime Domain Corruption [50.89405221574912]
Co-occurrence of domain shift and inaccessible variables runtime domain corruption seriously impairs generalizability of trained counterfactual predictor.
We build an adversarially unified variational causal effect model, named VEGAN, with a novel two-stage adversarial domain adaptation scheme.
We demonstrate that VEGAN outperforms other state-of-the-art baselines on individual-level treatment effect estimation in the presence of runtime domain corruption.
arXiv Detail & Related papers (2023-06-23T02:54:34Z) - Continuous-Time Modeling of Counterfactual Outcomes Using Neural
Controlled Differential Equations [84.42837346400151]
Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare.
Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions.
We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
arXiv Detail & Related papers (2022-06-16T17:15:15Z) - TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance.
We propose a versatile method that estimates joint distributions using an attention-based decoder.
We show that our model produces state-of-the-art predictions on several real-world datasets.
arXiv Detail & Related papers (2022-02-07T21:37:29Z) - Domain Conditional Predictors for Domain Adaptation [3.951376400628575]
We consider a conditional modeling approach in which predictions, in addition to being dependent on the input data, use information relative to the underlying data-generating distribution.
We argue that such an approach is more generally applicable than current domain adaptation methods.
arXiv Detail & Related papers (2021-06-25T22:15:54Z) - Time Series Domain Adaptation via Sparse Associative Structure Alignment [29.003081310633323]
We propose a novel sparse associative structure alignment model for domain adaptation.
First, we generate the segment set to exclude the obstacle of offsets.
Second, the intra-variables and inter-variables sparse attention mechanisms are devised to extract associative structure time-series data.
Third, the associative structure alignment is used to guide the transfer of knowledge from the source domain to the target one.
arXiv Detail & Related papers (2020-12-22T02:30:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.