Importance of Stationarity in a Time Series Process | MBA in HR Bangalore

Posted by Prof. Mangala V Reddy On 11/01/2023 19:00:35

Time series analysis is a specific way of analysing a sequence of data points collected over an interval of time. In time series analysis, analysts record data points at consistent intervals over a period of time rather than just recording the data points intermittently or randomly. Time series analysis typically requires a large number of data points to ensure consistency and reliability. MBA admission in Bangalore 2023

Time series forecasting of agricultural products plays a major role in the sustainability of agricultural production. Providing price forecasting information would help in decision-making for managing agricultural supplies and helping to improve purchasing behaviors of consumers. MBA finance in Bangalore

In addition to the seasonality in the production side caused by year-round cycle climate change, the prices are affected by the users’ preferences for products and suppliers’ trading strategies and behavior. These human factors do not always have seasonality but they could be represented as relations between events in past and the current status. MBA Bangalore

Stationarity of a Time Series process

Stationarity is a necessary condition for fitting a time-series model. Stationarity means that the stochastic properties, the moments (mean, variance, covariance) of the underlying time series are time-invariant. Time plot, Autocorrelation function (ACF), and Partial autocorrelation function (PACF) are used as a first attempt in determining the stationarity. For further confirmation, the augmented Dickey-Fuller test is used. MBA business analytics Bangalore

A statistical test for stationarity or a test for unit roots has been proposed by Dickey and Fuller (1979). The test is applied for the parameter in the auxiliary regression. MBA in entrepreneurship in Bangalore

D1yt=ryt-1+a1D1yt-1+et ……. (1)

Where D1 denotes the differencing operator i.e. D1yt= yt -yt-1

The relevant null hypothesis is r=0 i.e. the original series is non-stationary and the alternative is r<0 i.e. the original series is stationary. Usually, differencing is applied until the ACF shows an interpretable pattern with only a few significant autocorrelations. MBA in digital marketing in Bangalore

  1. ACF- Auto Correlation Function is used to measure the simple correlation between current observation (Yt) and observation from p periods before the current one (yt-p). That is for a given series Yt, autocorrelation at lag p is the correlation between the pair (yt, yt-p). it ranges from -1 to +1.
  2. PACF- Partial Auto Correlation Function is used to measure the degree of association between yt and yt-p when the y effects at other time lags 1,2,3,…,p-1 are removed.

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