1 00:00:00,780 --> 00:00:05,400 In this video, we will discuss the moving average method of forecasting. 2 00:00:07,180 --> 00:00:12,700 But no, that it is very different than the moving average moving method that we discussed earlier. 3 00:00:13,480 --> 00:00:15,340 So don't confuse it with that. 4 00:00:16,410 --> 00:00:18,910 Now, let's see what moving average forecasting is. 5 00:00:21,820 --> 00:00:29,740 Let's say we have a time cities and we use persistance model or the auto regression model to make predictions. 6 00:00:31,330 --> 00:00:37,990 I hope you remember what these two models, the persistence model, is basically using the last available 7 00:00:37,990 --> 00:00:40,720 value as the forecast for next time period. 8 00:00:42,460 --> 00:00:49,330 And auto regulation is running a regression model on the lagged values and using this model to predict 9 00:00:49,330 --> 00:00:50,290 the future values. 10 00:00:52,180 --> 00:00:56,950 So suppose you have already found out the prediction using any of these methods. 11 00:00:59,070 --> 00:01:02,520 Now, let's say the actual value of the time P plus one is Y. 12 00:01:03,250 --> 00:01:06,910 And the predicted value or time P plus one is Y hat. 13 00:01:08,470 --> 00:01:12,160 So after applying the model on the input, we have the value y. 14 00:01:14,290 --> 00:01:22,450 Another difference between Y her and the actual value Y is called residual or the forecasting at a. 15 00:01:24,480 --> 00:01:30,990 We can find all the residuals where we know both the actual values and the predicted values. 16 00:01:32,980 --> 00:01:40,590 Ideally, these set of residuals, that is the time series of residues should be white noise. 17 00:01:41,610 --> 00:01:45,150 There should not be any pattern, indeed, residual values. 18 00:01:46,320 --> 00:01:49,800 Ideally, there should not be any information left. 19 00:01:49,920 --> 00:01:50,820 Indeed, as it was. 20 00:01:52,620 --> 00:01:55,620 But practically, this may not always happen. 21 00:01:57,090 --> 00:02:04,870 Our model may leave some information, indeed, as it was, to extract that information and improve 22 00:02:04,870 --> 00:02:05,650 our forecast. 23 00:02:06,970 --> 00:02:09,670 We don't not forecasting model on residuals. 24 00:02:11,650 --> 00:02:14,020 We tried to forecast the future residuals. 25 00:02:15,220 --> 00:02:22,330 If there is any pattern individuals, this second level model, we are calling it the second level model. 26 00:02:22,810 --> 00:02:25,110 First level was on the accurate time series. 27 00:02:25,750 --> 00:02:27,340 We found the residual from that. 28 00:02:27,640 --> 00:02:31,360 And now we are building these taking level model on these residuals. 29 00:02:32,860 --> 00:02:39,670 So if there is any pattern, indeed, residuals of second level model will identify it and capture that 30 00:02:39,670 --> 00:02:42,130 information in the forecast of residues. 31 00:02:42,370 --> 00:02:49,180 We can then add back these forecasted residuals to our original forecast to get better forecast. 32 00:02:50,350 --> 00:02:52,420 This is the concept of moving average. 33 00:02:53,240 --> 00:03:00,040 I've listed down all the steps that are used in the moving average model that is initially we use air 34 00:03:00,100 --> 00:03:01,960 or persistent model to build a model. 35 00:03:03,840 --> 00:03:05,280 We didn't find it as it was. 36 00:03:05,850 --> 00:03:09,660 They are more along these networks and forecast these residuals. 37 00:03:10,850 --> 00:03:15,410 And lastly, we use the forecastable tools to update our initial forecast. 38 00:03:16,490 --> 00:03:21,770 This moving average method is often used along with auto regression. 39 00:03:23,270 --> 00:03:27,530 That is, auto regression is used as the first prediction model. 40 00:03:28,700 --> 00:03:32,300 And then moving averages used to update the initial predictions. 41 00:03:34,300 --> 00:03:40,730 When auto regression and moving average model is used together, it is called auto momentum. 42 00:03:43,270 --> 00:03:52,120 This model is quite popular, although I do remember that Ottmar can only work on cities without print 43 00:03:52,240 --> 00:03:53,110 and seasonality. 44 00:03:55,630 --> 00:03:58,070 So do remember to remove that before playing. 45 00:04:00,520 --> 00:04:06,790 In the next lecture, we will see a variant of AAMA, which automatically handles trend and seasonality 46 00:04:06,790 --> 00:04:07,210 also. 47 00:04:10,220 --> 00:04:17,480 So that is all this is moving average method, if you like, residual values are used to forecast. 48 00:04:17,700 --> 00:04:19,340 There's Duell for the next time period. 49 00:04:20,150 --> 00:04:27,230 This forecasted residual is also ordered in the original forecast to get a new and improved forecast. 50 00:04:28,460 --> 00:04:31,210 Now, let's see how to implement this in our software.