# ACT-Tensor: Tensor Completion Framework for Financial Dataset Imputation

arXiv: https://arxiv.org/abs/2508.01861 · HTML: https://mlsanity.com/paper/2508.01861/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 54 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [SAITS: Self-Attention-based Imputation for Time Series](https://mlsanity.com/paper/2202.08516/) (2022)
- [Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery](https://mlsanity.com/paper/1307.5870/) (2013)
- [Provable Tensor Factorization with Missing Data](https://mlsanity.com/paper/1406.2784/) (2014)
- [Spectral algorithms for tensor completion](https://mlsanity.com/paper/1612.07866/) (2016)
- [Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors](https://mlsanity.com/paper/1404.4667/) (2014)
- [Low-Rank Tucker Approximation of a Tensor From Streaming Data](https://mlsanity.com/paper/1904.10951/) (2019)
- [Tensor Completion Made Practical](https://mlsanity.com/paper/2006.03134/) (2020)
- [Constrained Factor Models for High-Dimensional Matrix-Variate Time Series](https://mlsanity.com/paper/1710.06075/) (2017)
- [Statistical Inference for High-Dimensional Matrix-Variate Factor Model](https://mlsanity.com/paper/2001.01890/) (2020)
- [CP Factor Model for Dynamic Tensors](https://mlsanity.com/paper/2110.15517/) (2021)
- [Semi-parametric TEnsor Factor Analysis by Iteratively Projected Singular Value Decomposition](https://mlsanity.com/paper/2007.02404/) (2020)
- [Modeling Dynamic Transport Network with Matrix Factor Models: with an Application to International Trade Flow](https://mlsanity.com/paper/1901.00769/) (2019)
- [Modeling Multivariate Spatial-Temporal Data with Latent Low-Dimensional Dynamics](https://mlsanity.com/paper/2002.01305/) (2020)
- [Distributed Tensor Principal Component Analysis with Data Heterogeneity](https://mlsanity.com/paper/2405.11681/) (2024)
- [High-Dimensional Tensor Discriminant Analysis with Incomplete Tensors](https://mlsanity.com/paper/2410.14783/) (2024)
- [Conditional Prediction ROC Bands for Graph Classification](https://mlsanity.com/paper/2410.15239/) (2024)
- [Conditional Uncertainty Quantification for Tensorized Topological Neural Networks](https://mlsanity.com/paper/2410.15241/) (2024)
- [Principal Components and Regularized Estimation of Factor Models](https://mlsanity.com/paper/1708.08137/) (2017)

## Abstract (arXiv)

Missing data in financial panels presents a critical obstacle, undermining asset-pricing models and reducing the effectiveness of investment strategies. Such panels are often inherently multi-dimensional, spanning firms, time, and financial variables, which adds complexity to the imputation task. Conventional imputation methods often fail by flattening the data's multidimensional structure, struggling with heterogeneous missingness patterns, or overfitting in the face of extreme data sparsity. To address these limitations, we introduce an Adaptive, Cluster-based Temporal smoothing tensor completion framework (ACT-Tensor) tailored for severely and heterogeneously missing multi-dimensional financial data panels. ACT-Tensor incorporates two key innovations: a cluster-based completion module that captures cross-sectional heterogeneity by learning group-specific latent structures; and a temporal smoothing module that proactively removes short-lived noise while preserving slow-moving fundamental trends. Extensive experiments show that ACT-Tensor consistently outperforms state-of-the-art benchmarks in terms of imputation accuracy across a range of missing data regimes, including extreme sparsity scenarios. To assess its practical financial utility, we evaluate the imputed data with an asset-pricing pipeline tailored for tensor-structured financial data. Results show that ACT-Tensor not only reduces pricing errors but also significantly improves risk-adjusted returns of the constructed portfolio. These findings confirm that our method delivers highly accurate and informative imputations, offering substantial value for financial decision-making.