[R] SAITS: Self-Attention-based Imputation for Time Series. Expert Systems with Applications, 219:119619, 2023.

• SAITS

  9 256 6.6 Python

  The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

Missing values in time series collected from the real world are common to see and very pesky. A new state-of-the-art and fast neural network called "SAITS“ is proposed to help impute missing data in partially-observed multivariate time series. The paper has been peer-reviewed and published in the journal Expert Systems with Applications (DOI link). The full paper is available on arXiv at this URL. The code is open source on GitHub https://github.com/WenjieDu/SAITS/.

• PyPOTS

  50 668 9.6 Python

  A Python toolbox/library for reality-centric machine/deep learning and data mining on partially-observed time series with PyTorch, including SOTA neural network models for science tasks of imputation, classification, clustering, forecasting & anomaly detection on incomplete (irregularly-sampled) multivariate time series with NaN missing values/data

You're talking about the end-to-end task on partially-observed time series (POTS) like classification. But sometimes people need to know approximated values of missing parts. Imputation works for them. And imputation can help with downstream tasks (e.g. classification) as well, please refer to the section “4.4.2 Downstream Classification Task” in the SAITS paper. BTW, we include end-to-end methods for modeling POTS data in PyPOTS that may interest you, this library supports four tasks of classification, forecasting, clustering, and imputation.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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