Employing Long-Short-Term Memory Cells for Univariate Time Series Imputation in Weather Sensors Data

Abstract

Data imputation has attracted considerable interest due to the importance of data quality, a key challenge in data science. Various statistical methods, and more recently machine learning techniques, have been developed to address the issue of missing values. In this study, we present an imputation method that integrates forecasting and backcasting using Long-Short-Term Memory (LSTM) architecture for predicting blocks of consecutive missing values. The proposed method was evaluated on a randomly generated absent group of data from a weather dataset. In this context, we assessed different hyperparameters using regression metrics. Initially, we trained and tested the models with varying data and sequence sizes on distinct units of missing data, subsequently applying the method to other units with specific data and sequence sizes. Additionally, we substituted the LSTM model with other machine learning algorithms applying, the same method, and we compared the results. Finally, we tested the method on missing blocks from a dataset obtained from the Digital Ocean Lab (DOL) weather station. Our findings indicate that this method effectively provides a reasonable estimation of missing values in time series datasets.