In: Statistics and Probability
We have introduced several models to handle time series data. Please choose one of them (Time Series Data forecasting – Moving Averages) and discuss an application of the model. thank you
Time Series Data Forecasting - Moving Averages is a smoothing technique applied to Time Series to remove the fine - grained variation between time steps, by removing the noise and better expose the signal of the underlying causal processes.
Creating a Moving Average involves creating a new Time Series where the values are composed of average of new observations in the original time series.
AN APPLICATION OF THE MODEL:
Consider : monthly obervations of price over a year. i.e., 12 values of price with equal interval of time. Suppose, after plotting the data with x axis as periods: 1,2,..12 and y axis as price, we note that it has upward trend with a number of peaks and valleys.The larger the interval, the more the peaks, and valleys are smoothed out. The smaller the interval, the closer the moving averages are to the actual data points. Thus, this application of Moving Averages deals with historical data having many peaks and valleys.