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The RePEc plagiarism page In: Bruce L. Bowerman, Richard T. O'Connell and Anne B. Koehler, Editors, Forecasting, time series, and regression: an applied approach (4th edition), Duxbury Press (2005) ISBN 0-534-40977-6 686 pagesBrian Sloboda` (Obfuscate( 'email.phoenix.edu', 'bsloboda' ))International Journal of Forecasting, 2005, vol. 21, issue 2, 391-392Date: 2005
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Now Ive read some things about Time Series Analysis and although there definetly is a difference I cant really grasp it. So most examples Ive read are on univariate time series modeling. The idea I understood is, you take the earnings and create a shifted column of that same value for your X.
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Now what I have is a dataset with 80 columns and what I have done is use the X for time t to predict y for t+1. However according to time series modelling I "want" to consider older month too. So I want to predict t+1 with t,t-1,t-2,t-4 and then use that information to create the prediction for t+2 and so on.
Did I understand this correctly? Is the goal of time series analysis not to create a forecast for the next point in time which I am doing right now but rather a longer preiod of time, so you build the predictions up with the goal to have for example a 1 year forecast.
Last but not least, I worked a lot on my current solution (I am new to this due to a small university program) is that solution not suitable and was everything Ive done so far wrong/useless? Isnt using a 1 month time shift and predicting the next month just the same as the "first step" of a time series forecast? Or canI just add that prepocessing step and keep my models the same?
The rates of respiratory syncytial virus hospitalizations among patients younger than 2 years with CLDI by quarter from 1998 to 2008. Actual rates and predicted rates from time series regression model.
The rates of respiratory syncytial virus and AB and bronchiolitis hospitalizations among patients younger than 2 years with CLDI by quarter from 1998 to 2008. Actual rates and predicted rates from time series regression model.
Different time series analysis of daily air pollution of Isfahan city were performed in this study. Descriptive analysis showed different long-term variation of daily air pollution. High persistence in daily air pollution time series were identified using autocorrelation function except for SO2 which seemed to be short memory. Standardized air pollution index (SAPI) time series were also calculated to compare fluctuation of different time series with different levels. SAPI time series indicated that NO and NO2, CH4 and non-CH4 have similar time fluctuations. The effects of weather condition and vehicle accumulation in Isfahan city in cold and warm seasons are also distinguished in SAPI plots.
The limitless of air pollution range and sources have forced pollution managers to apply new methods in air pollution control and monitoring. Development and use of statistical and other quantitative methods in the environmental sciences have been a major communication between environmental scientists and statisticians (Herzberg and Frew, 2003). This approach is called top-down approach which starts with statistical analysis of collected air pollution data (Lee, 2002). In recent years many statistical analysis have been used to study air pollution as a common problem in urban areas. The common descriptive statistical approach used for air quality measurement and modeling is rather limited as a method to understand the behavior and variability of air quality. Different techniques have been used for air quality monitoring systems. Voigt et al., 2004 applied principle component analysis in order to evaluate different air pollutants like ozone (O3), nitrogen dioxide (NO2) and carbon monoxide (CO) in 15 European member states. Many investigators have used probability models to explain temporal distribution of air pollutants (Bencala and Seinfeld, 1979, Yee and Chen, 1997). Time series analysis is a useful tool for better understanding of cause and effect relationship in environmental pollution (Schwartz and Marcus, 1990, Salcedo et al., 1999, Kyriakidis and Journel, 2001). The principle aim of time series analysis is to describe the history of movement of a particular variable in time. Many authors have tried to detect changing behavior of air pollution through time using different techniques (Salcedo et al., 1999, Hies et al., 2000, Kocak et al., 2000, among others). Many others have tried to relate air pollution to human health through time series analysis (Gouveia and Fletcher, 2000, Roberts, 2003, Touloumi et al., 2004). The object of this study is to examine daily time series analysis of some air pollutants in Isfahan City, in the center of Iran. The average daily air pollution concentrations (APC) of SO2, CO, CH4, NO2, NO, non-CH4 and O3 were selected from March 2003 to March 2004.
(cv) helps the investigator to overcome the problem of different levels and units of variables in order to compare them. Coefficient of Skewness (cs) and Kurtosis (ck) are other measures which may be used to characterize the symmetry and flatness of the probability density function of a time series, respectively (Windsor and Toumi, 2001). Because of high order, kurtosis is particularly sensitive to extremes or intermittent fluctuations and, therefore, a useful indicator of intermittency. Highly intermittent time series have a higher kurtosis. However, descriptive analysis is of rather limited value due to the large variability associated with air quality data through time (Salcedo, et al., 1999).
A time series is a set of observations that are arranged chronologically. In time series analysis, the order of occurrence of the observation is crucial. If the chronological ordering of data were ignored, much of the information contained in time series would be lost. A variety of different important terminologies in time series analysis are existence such as stationarity, periodicities and trend which fall into temporal categories of air pollutant concentration (Klemm and Lange, 1999, Lee, et al., 2003). Stationarity of a process can be qualitatively interpreted as a form of statistical equilibrium. Therefore, the statistical properties of the process are not a function of time. For interpretation purposes, it is often useful to plot Autocorrelation function (ACF) against lag time, K. ACF is a simple graphical method to find time relationship of an event. The sample autocorrelation coefficient is written as (Box and Jenkins,1976):
The above analysis will show us whether air pollutant are dependent on time or not but in air pollution time series analysis, it would be useful to find time periods of risky air pollution levels. In order to compare different air pollutants with different levels and units, we use standardized air pollution index which is written as follows:
WherePi is the pollutant concentration at time i, P and are the mean and standard deviation of the series and SAPI is the StandardizedAir Pollution Index. Standardized air pollution index is not only useful to determine risky periods of air quality characteristics but to define the risky periods as well. It is also possible to determine air pollution interaction through time using cumulative SAPI. The ASAPI will disclose the cumulative risky periods of air quality and is useful in air health monitoring.
The descriptive statistics of selected daily air pollutions are presented in Table 1. As the coefficient of variation (cv) is a measure of variation over time (Lee, 2002), the comparison of pollutants indicates that NO has the highest variation over time while CH4 has the smallest variation. The degree of variation decrease in the order: NO< NO2< SO2< TSP < CO < non-CH4 < O3 < CH4. This variation may be the result of variation in generating resources or weather condition. The coefficient of skewness (cs) measures the relativse skewness of frequency distribution; as time series, air pollution concentration data are characterized buy strongly right-skewed frequency distribution in this study likeother previous studies by Georgopoulos and Seinfeld (1982) and Lee (2002). The degree of rightskewnessdecrease in order: NO < NO2 < SO2 < non-CH4 < TSP < CO < CH4 < O3.
The first step in time series analysis is to draw time series plot. Time series plot can give a preliminary understating of the time behavior of the series. Fig.1. shows time series plot of selected time series air pollution concentration. This Figure shows different time behavior of air pollutants. For example, the concentration of O3 and TSP seem to have a similar trend from the beginning of the year to the end but the maximum and minimum concentrations occur in different time. It is also obvious that SO2 and NO have not a significant trend through time. The fluctuations of NO2, SO2 are more irregular at the end of the year but the fluctuation of non-CH4 is more obvious at the beginning of the year. e24fc04721
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