Analysis of time series data Research Paper Example | Topics and Well Written Essays - 3000 words

Analysis of time series data - Research Paper Example Statistical forecasting methods depend on the fact that a time series could be rendered stationary. A stationary time series is a time series whose statistical properties such as mean, variance, autocorrelation, etc. remain constant over time. Statistical forecasting methods compute these stationary time statistical properties from its past values, and use them to predict future values since they will remain the same in the future. Obtaining statistical values such as means, variances, and correlation from non-stationary time series are non-meaningful. This is because such statistics represent only the past but not the future. For example, if the series is consistently increasing over time, the sample mean and variance will grow with the size of the sample, and they will always underestimate the mean and variance in future periods. For this reason much caution should be given to extrapolate regression models fitted to non-stationary data. However, most naturally created time series are non-stationary when expressed in their original units of measurements. They exhibit trends, cycles, random-walking and non-stationary behavior. They remain non-stationary even after deflation or seasonal adjustment. Transforming Non-Stationary Time Series: Non-stationary time series could be converted into stationary ones using mathematical transformations. Predictions for the stationarized series can then be "untransformed," by reversing whatever mathematical transformations were previously used, to obtain predictions for the original series. Thus, finding the sequence of transformations needed to stationarize a time series often provides important clues in the search for an appropriate forecasting model. Trend-Stationary Time Series: It is a time series with a stable long-run trend and reverts back to the trend line following a disturbance. It is stationarized by de-trending. De-treding involves fitting a trend line then subtracting it from the time series. Another way would include the time index as an independent variable in a regression or ARIMA model. Difference-stationary Time