figure - smoothening using moving average the plot in figure - shows the smoothened visits time series the smoothened series captures the overall structure of the original series all the while reducing the random variation in it you are encouraged to explore and experiment with different window sizes and compare the results depending on the use case and data at handwe also try different variations of moving average like centered moving averagedouble moving averageand so on apart from different window sizes we will utilize some of these concepts when we deal with actual use cases in the coming sections of the exponential smoothing moving average based smoothening is effective yet it is pretty simple preprocessing technique in case of moving averageall past observations in the window are given equal weight unlike the previous methodexponential smoothening techniques apply exponentially decreasing weights to older observations in simple wordsexponential smoothening methods give more weight to recent past observations as compared to older observations depending on the level of smoothening requiredthere may be one or more smoothening parameters to set in case of exponential smoothening