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Time series is a series of points that are indexed to keep track of changes and deviations that happened over a specified period of time. The gaps that you observe between these periods would be consistent. This is the reason for its calling as a series of discrete information. Time series are represented using line charts. These are widely used to forecast the market of the company, earthquake prediction, weather forecasting, Econometrics, control engineering, communication engineering, astronomy, etc. Time series analysis is a powerful tool that is available to the statistician to draw a conclusion and prepare the right strategies. There are different methods that are embraced by statisticians to study time series. These include - linear and non-linear methods, parametric and non-parametric methods and the last one are univariate and multivariate methods.
Time series analysis contains a myriad of methods that are used to analyze the time series data to get precise statistics and other key traits of the data. Time series forecasting is widely used to predict future values based on past values. The best example of time series forecasting is in Econometrics. This helps you to find the opening price of a stock based on its past performance. Time series analysis is very different from that of spatial data analysis where you can relate the observations to the geographical locations. The time series model would use the natural one-way ordering of time to ensure that the values over a specific period of time would be derived based on the past values over the future values.
Time series analysis is the tool that is used by statisticians along with strategic managers.
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Time series analysis is used in various fields, including finance, economics, and medicine. The time series data in economics will help you to calculate monthly data of employment, GPD data, etc. This is also used in finance to check the stock prices, exchange rates, etc. The time series analysis carried out in environmental science will help you to find the daily rainfall data, temperature information, etc. The time series analysis is the best method that is used to draw a line chart for the variables against time (days, weeks, months, or years).
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Time series analysis is a unique tool that helps you to use time series analysis in different applications.
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|Uses of time series||Autocorrelation|
|Stationarity||Basic time series models: AR, MA, ARMA|
|Trend removal and seasonal adjustment||Invertibility|
|Forecasting||Financial time series and the (g)arch model|
|Non-stationarity||Bivariate time series|
|Time Series Analysis and Forecasting||Applied Business Research and Statistics|
|Stochastic Modeling and Bayesian Inference||Autocorrelation|
|Diagnostic checking and linear prediction||Non-stationarity and differencing spectral analysis|
|R code and S-Plus assignment help||Time Series Analysis and Forecasting|
|POM/QM assignment help||Regression Analysis|
|Data analysis and preprocessing||Parameter estimation|
|Glosten-Jagannathan-Runkle (GJR)||Exponential GARCH (EGARCH)|
|Vector autoregressive (VAR)||Vector autoregressive moving average (VARMA)|
|Structural VARMAX (SVARMAX)||Vector autoregressive moving average with exogenous inputs (VARMAX)|
|Vector error-correction (VEC)||Vector moving average (VMA)|