Uncertain Time Series in Weather Prediction

The paper reviews about methods have been implemented on uncertain time series data in weather prediction. The aim of uncertain time series analysis is to formulate uncertain data in order to gain knowledge, fit low dimensional models, and do prediction. Euclidean distance, particle swarm optimization, data mining, and Monte Carlo simulation are methods that have been compared to investigate the best ways of predicting. These methods have been implemented since early 1900s. This paper discusses on the performance of every methods.