Time Series Analysis with R
نویسندگان
چکیده
The purpose of our article is to provide a summary of a selection of some of the high-quality published computational time series research using R. A more complete overview of time series software available in R for time series analysis is available in the CRAN task views. If you are not already an R user, this article may help you in learning about the R phenomenon and motivate you to learn how to use R. Existing R users may find this selective overview of time series software in R of interest. Books and tutorials for learning R are discussed later in this section. An excellent online introduction from the R Development Core Team is available as well as extensive contributed documentation.
منابع مشابه
On The Behavior of Malaysian Equities: Fractal Analysis Approach
Fractal analyzing of continuous processes have recently emerged in literatures in various domains. Existence of long memory in many processes including financial time series have been evidenced via different methodologies in many literatures in past decade, which has inspired many recent literatures on quantifying the fractional Brownian motion (fBm) characteristics of financial time series. Th...
متن کاملExploratory analysis of PM2.5 variation trend of Tehran, Iran, in various time series and its relation with cardiovascular mortality rate using R software
Among the numerous air pollutants, the strongest proof for adverse health effects has been reported for particulate matter (PM). The aim of this study was the exploration of short-term associations of air pollution with mortalities due to cardiovascular diseases (CVD) in Tehran, Iran, based on hospital and census data from 2007 to 2013. This descriptive and analytical research was conducted in ...
متن کاملSome New Methods for Prediction of Time Series by Wavelets
Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...
متن کاملSpectral Estimation of Stationary Time Series: Recent Developments
Spectral analysis considers the problem of determining (the art of recovering) the spectral content (i.e., the distribution of power over frequency) of a stationary time series from a finite set of measurements, by means of either nonparametric or parametric techniques. This paper introduces the spectral analysis problem, motivates the definition of power spectral density functions, and reviews...
متن کاملResidual analysis using Fourier series transform in Fuzzy time series model
In this paper, we propose a new residual analysis method using Fourier series transform into fuzzy time series model for improving the forecasting performance. This hybrid model takes advantage of the high predictable power of fuzzy time series model and Fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...
متن کاملPredicting the Incidence and Trend of Breast Cancer Using Time Series Analysis for 2007-2016 in Qazvin
Introduction: Breast cancer is the most common cancer and the second leading cause of death in women worldwide. The aim of this study was to analyze the trend and predict the incidence of breast cancer using time series analysis. Methods: In this study, data on breast cancer incidence in Qazvin province between 2007 and 2016 were analyzed using time series analysis with autoregressive integrate...
متن کامل