Papers › Decoupling Local and Global Representations of Time Series

Decoupling Local and Global Representations of Time Series

4 Feb 2022arXiv:2202.02262archive 2025-07-28

Sana Tonekaboni, Chun-Liang Li, Sercan Arik, Anna Goldenberg, Tomas Pfister

Real-world time series data are often generated from several sources of variation. Learning representations that capture the factors contributing to this variability enables a better understanding of the data via its underlying generative process and improves performance on downstream machine learning tasks. This paper proposes a novel generative approach for learning representations for the global and local factors of variation in time series. The local representation of each sample models non-stationarity over time with a stochastic process prior, and the global representation of the sample encodes the time-independent characteristics. To encourage decoupling between the representations, we introduce counterfactual regularization that minimizes the mutual information between the two variables. In experiments, we demonstrate successful recovery of the true local and global variability factors on simulated data, and show that representations learned using our method yield superior performance on downstream tasks on real-world datasets. We believe that the proposed way of defining representations is beneficial for data modelling and yields better insights into the complexity of real-world data.

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airq_data_loader googleinterns/local_global_ts_representation/gl_rep/data_loaders.py official repository unverified Apache-2.0 (permissive) · a362d33e62ef4c5e · report
block_labels googleinterns/local_global_ts_representation/evaluations/daily_rain.py official repository unverified Apache-2.0 (permissive) · 71db13da0b5a7c00 · report
classification_exp googleinterns/local_global_ts_representation/evaluations/classification.py official repository unverified Apache-2.0 (permissive) · 50fc56605d9eba14 · report
gen_counterfactual googleinterns/local_global_ts_representation/evaluations/counterfactual.py official repository unverified Apache-2.0 (permissive) · 4ce3da76a4ebf191 · report
har_data_loader googleinterns/local_global_ts_representation/gl_rep/data_loaders.py official repository unverified Apache-2.0 (permissive) · 781e90f49ea1254e · report
matern_kernel googleinterns/local_global_ts_representation/gl_rep/gp_kernel.py official repository unverified Apache-2.0 (permissive) · fa21649c4bac04b2 · report
periodic_kernel googleinterns/local_global_ts_representation/gl_rep/gp_kernel.py official repository unverified Apache-2.0 (permissive) · 256f451620824f7b · report
rbf_kernel googleinterns/local_global_ts_representation/gl_rep/gp_kernel.py official repository unverified Apache-2.0 (permissive) · d296bafddac8ff19 · report
run_classifier_epoch googleinterns/local_global_ts_representation/evaluations/classification.py official repository unverified Apache-2.0 (permissive) · 90c4c5d829561335 · report
run_epoch googleinterns/local_global_ts_representation/evaluations/daily_rain.py official repository unverified Apache-2.0 (permissive) · d279b9f95cb9561b · report
simulation_loader googleinterns/local_global_ts_representation/gl_rep/data_loaders.py official repository unverified Apache-2.0 (permissive) · 349f39eb25a436a5 · report

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