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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
This repository provides regression models to 'estimate' the most likely values for imputation purposes. The models are statistically representative of the German non-domestic building stock and cover DIBS input variables with a larger probabolity of missing values.
This repository provides Lastools and R based scripts for 3D LiDAR data processing and imputation modelling for yield prediction at plot and individual tree levels