MVI and Forecast Precision Upgrade of Time Series Precipitation Information for Ubiquitous Computing

Abstract

Missing values are the major problem in a time - series dataset that lead to a very typical phenomenon in a dataset analysis. Better analysis of a dataset in ubiquitous computing can only be achieved after efficiently handling the missing values. This paper envisioned to present the missing values imputation (MVI) in the monthly rainfall dataset in India. Generally, the approximation of the missing values was completed using Non-linear principal component analysis, which is having a scope of enhancement. Kalman filter using Arima model to impute missing values is a better way that can be further improved by using Extended Kalman filtering. Further, a rainfall prediction is carried out using LSTM along with three different optimizers including stochastic gradient descent (SGD), RSM-Prop, and ADAM optimizers. In addition, a comparative prediction of rainfall is depicted which shows the combination of Extended Kalman imputation, LSTM, and ADAM optimizer to outperformance in prediction.