File Name: time series analysis forecasting and control box .zip
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Pourmousa, R. Background and Aim: Air pollution is one of the most important problems of big cities in developing countries and can have several negative health effects on humans. Therefore studying these pollutants can help in developing programs for air pollution control. The aim of this study was to estimate and predict the changes of air pollutants in Kerman, Iran. Then the data was calculated as averages per month and by incorporating time series models, predictions were done for each pollutant.
Results: All of the pollutants were steady in Kerman, except CO which is significantly decreasing and PM 10 which is increasing.
All of the pollutants had a seasonal pattern. Conclusion: The production of ambient CO is decreasing in Kerman and one reason is probably replacing and retiring old automobiles. However PM 10 is increasing in Kerman and in most seasons it is above standard and therefore control initiatives should be implemented. Remember me Create Account Reset Password. Forecasting ambient air pollutants by time series models in Kerman, Iran. Hamilton, JD.
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Time Series Analysis. Forecasting and Control. George E. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject.
In Mathematics , a time series is a series of data points indexed or listed or graphed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides , counts of sunspots , and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via run charts a temporal line chart. Time series are used in statistics , signal processing , pattern recognition , econometrics , mathematical finance , weather forecasting , earthquake prediction , electroencephalography , control engineering , astronomy , communications engineering , and largely in any domain of applied science and engineering which involves temporal measurements. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.
Anderson, H. Air pollution and mortality: A history. Atmospheric Environment, 43 , pp. Box, GEP. Duenas, C.
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