Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data

Abstract

We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for dealing with the bias is the inverse propensity score (IPS) estimation. However, the existing propensity-based methods can suffer significantly from the propensity estimation bias. In fact, most of the previous IPS-based methods require some amount of missing-completely-at-random (MCAR) data to accurately estimate the propensity. This leads to a critical self-contradiction; IPS is ineffective without MCAR data, even though it originally aims to learn recommenders from only missing-not-at-random feedback. To resolve this propensity contradiction, we derive a propensity-independent generalization error bound and propose a novel algorithm to minimize the theoretical bound via adversarial learning. Our theory and algorithm do not require a propensity estimation procedure, thereby leading to a well-performing rating predictor without the true propensity information. Extensive experiments demonstrate that the proposed algorithm is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data. Full version of the paper (including the appendix) is available at: https://arxiv.org/abs/1910.07295.

Time-Series Data Imputation via Realistic Masking-Guided Tri-Attention Bi-GRU

“…Based on the reset gate, current input c replace , and past hidden state h t−1 , we obtain the candidate hidden state using Eq. (19). Finally, in Eq.…”

“…[32] proposed an unsupervised autoencoder model named MTSIT based on Transformer, which jointly reconstructs and computes multivariate time series using unlabeled data. Regarding spatiotemporal data [19], Cross-Dimensional Self-Attention (CDSA) [18] was proposed, which is an effective imputation method that not only captures temporal dependencies but also leverages the geographic relationships among sensors to fill in missing values in time series data. To address the problem of irregularly sampled time series, a novel approach called NRTSI [24] was proposed.…”