My Good First Issue mentions (0)

PyPOTS mentions (3)

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

  Absolutely my pleasure! Please pay a visit to the toolbox PyPOTS https://pypots.com if you're interested in modelling partially-observed time series (POTS). It deserves your attention ;-). Source: almost 2 years ago

• 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 impute missing data in partially-observed multivariate time series. The code is open source on GitHub.

  If your research lies in time-series modeling, you may also be interested in the work PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series https://pypots.com/. Its full paper is available on arXiv as well https://arxiv.org/abs/2305.18811, which has been peer-reviewed and accepted by the 9th SIGKDD international workshop Mining and Learning from Time Series (MiLeTS'23). Source: almost 2 years ago

• We built PyPOTS: an open-source toolbox for data mining on partially-observed time series

  Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modelling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated... Source: almost 2 years ago