Time Series Missing Imputation with Multivariate Radial Based Function Neural Network
Primary Area: general machine learning (i.e., none of the above)
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Keywords: Time series data, missing data, Radial Basis Function, Imputation
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Abstract: Researchers have been persistently working to address the issue of missing values in time series data. While numerous models have been proposed, they often come with challenges related to assumptions about the model or data and the instability of deep learning. This paper introduces an imputation model that can be utilized without explicit assumptions. Our imputation model is based on the Radial Basis Function (RBF) and learns local information from timestamps to create a continuous function. Additionally, we incorporate time gaps to facilitate learning information considering the missing terms of missing values. We name this model the Missing Imputation Multivariate RBFNN (MIM-RBFNN). However, MIM-RBFNN relies on a local information-based learning approach, which presents difficulties in utilizing temporal information. Therefore, we propose an extension called the Missing Value Imputation Recurrent Neural Network with Continuous Function (MIRNN-CF) using the continuous function generated by MIM-RBFNN. We evaluate the performance using two real-world datasets and conduct an ablation study comparing MIM-RBFNN and MIRNN-CF.
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Submission Number: 1685
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