Pm2.5 Time Series Imputation with Deep Learning and Interpolation - Anibal Flores; Hugo Tito-Chura; Deymor Centty-Villafuerte; Alejandro Ecos-Espino (Computers, 2023)
Commonly, regression for time series imputation has been implemented directly through regression models, statistical, machine learning, and deep learning techniques. In this work, a novel approach is proposed based on a classification model that determines the NA value class, and from this, two types of interpolations are implemented: polynomial or flipped polynomial. An hourly pm2.5 time series from Ilo City in southern Peru was chosen as a study case. The results obtained show that for gaps of one NA value, the proposal in most cases presents superior results to techniques such as ARIMA, LSTM, BiLSTM, GRU, and BiGRU; thus, on average, in terms of R2, the proposal exceeds implemented benchmark models by between 2.4341% and 19.96%. Finally, supported by the results, it can be stated that the proposal constitutes a good alternative for short-gaps imputation in pm2.5 time series.