Data Analysis in Los Angeles Long Beach with Seasonal Time Series Model

Air pollution has been a huge problem for a long time, more and more scientists focus on this hot topic, In this paper we presented a series data analysis methods for Los Angeles Long Beach datasets by Seasonal ARIMA(autoregressive integrated moving average) model and MCMC(Markov chain Monte Carlo) method. The MCMC methods are studied with LA long beach air pollution PM 2.5 traffic from 1997 to 2008 observations. The conclusion illustrated that experimental results indicate that the seasonal ARIMA model can be an effective way to forecast air pollution, and also know the MCMC model fitting the datasets very significantly. This approach applied to a large class of utility functions and models for Air pollution and traffic fields.