Now we need to do some analysis to find out the exact non-stationary and seasonality in the data. We can infer from the graph itself that the data points follows an overall upward trend with some outliers in terms of sudden lower values. This is how the actual dataset looks like: As it is a monthly data so ‘frequency=12’.
start gives the starting time of the data, in this case, its Jan 2011. Where RawData is the univariate data which we are converting to time series. To do so we need to run the following command in R: tsData = ts(RawData, start = c(2011,1), frequency = 12)
#Eviews regression analysis series
The first step in time series data modeling using R is to convert the available data into time series data format. Auto-regression is all about regression with the past values. Data should be univariate – ARIMA works on a single variable. A white noise series and series with cyclic behavior can also be considered as stationary series. Data should be stationary – by stationary it means that the properties of the series doesn’t depend on the time when it is captured. Auto Regressive (AR) terms refer to the lags of the differenced series, Moving Average (MA) terms refer to the lags of errors and I is the number of difference used to make the time series stationary. Holt winters exponential smoothing (single, double and triple)ĪRIMA is the abbreviation for AutoRegressive Integrated Moving Average. Techniques used for time series analysis: Moreover, time series analysis can be classified as:
Time series data analysis means analyzing the available data to find out the pattern or trend in the data to predict some future values which will, in turn, help more effective and optimize business decisions. Time series data are data points collected over a period of time as a sequence of time gap.
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