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A real time series is a sequence of data points which is made over a time interval (continuous), out of successive measurements across the given interval. It uses equal spacing between every two consecutive measurements, and with each time unit existing within the time interval having at most one data point. There are various examples of time series are counts of sunspots, ocean tides and daily closing value of Dow Jones Industrial Average.

There are various non –examples such as in the height measurements of a finite group of people, where as each height is measured over a period of time and the data set contains one record of each person at a time. The panel data is a multidimensional data set, a general class. But the Real time series data is a one dimensional panel which is similar to cross sectional dataset. Yet the characteristics exhibited by data set is of both time series data and panel data. One way that exists to tell is to ask why a record is so unique as compared to other records. If we determine that a unique record needs an additional identifier as well as time data field that is unrelated to time (stock symbol, student ID or country code), then it is considered as a panel data candidate.

**Topics Help For Real Time Series Assignment Help:**

- Real time series analysis ,Stochastic process ,characteristics, Autoregressive-moving average models ARMA (p,q), Coefficient estimation in ARMA (p,q) processes. Box-Jenkins’ approach, Forecasting in the framework of Box-Jenkins model
- Non-stationary time series, unit root problem, Vector autoregression model and co-integration etc,Autocorrelations, time-domain model fitting including autoregressive and moving average processes, spectral methods
- Effect of time, series correlations on other kinds of statistical inference,estimation of means and regression coefficients., Models for time series , Models of stationary processes,Spectral methods, Estimation of the spectrum
- Linear filters, Estimation of trend and seasonality, Fitting ARIMA models, State space models,theory and application of time series methods in econometrics. univariate stationary and non-stationary models, vector autoregressions,
- frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE):

**Real Time Series Assignment Help: **

- simulated method of moments, maximum likelihood and Bayesian approach.,univariate stationary, univariate non-stationary, vector autoregressions, frequency domain analysis, persistent time series, structural breaks,
- dynamic stochastic general equilibrium, DSGE, Bayesian, econometrics, VAR, unit root, prediction regression, GMM, MCMC,time series analysis :descriptive methods, , plots, , smoothing, , differencing; ,
- autocorrelation function, correlogram and variogram, periodogram; , estimation and elimination of trend and seasonal components; ,stationary processes, , modelling and forecasting with autoregressive moving average (ARMA) models
- spectral analysis, fast Fourier transform, , periodogram averages and or smooth estimates of spectrum;, time- invariant linear filters; , non-stationary and seasonal time series models; , ARIMA processes, identification
- estimation and diagnostic checking, , forecasting, , including extrapolation of polynomial trends, , exponential smoothing, , Box-Jenkins approach.,Simple descriptive techniques,stationarity,Moving average ,Autoregressive ,
- ARMA and ARIMA models,Estimating the autocorrelation function,Fitting ARIMA models,Forecasting,Exponential smoothing,Forecasting from ARIMA models,The spectral density function,Periodogram,
- Spectral analysis,State-space models,Dynamic linear models,Kalman filter,Stationary processes,Autoregressive processes,Moving average processes,White noise,Turning point test,Purely indeterministic processes,Autocovariance function,Distributions of the ACF and PACF,Distribution of spectral estimates ,Fast Fourier transform,Box-Jenkins procedure.