1 00:00:01,080 --> 00:00:09,180 Now, let's discuss the Atima model are stands for auto regression, integrated moving average. 2 00:00:10,410 --> 00:00:13,860 You know, about auto regression and the moving average part of it. 3 00:00:15,090 --> 00:00:16,740 What does integrated stand for? 4 00:00:18,330 --> 00:00:23,220 This method is called integrated because it includes the method or different thing. 5 00:00:24,870 --> 00:00:27,010 You may remember different thing from previous Lichter's. 6 00:00:27,420 --> 00:00:32,130 Different thing was used to be seasonal seasonals and data and data. 7 00:00:34,290 --> 00:00:40,560 And I've been repeating this several times that auto regression and moving others models should not 8 00:00:40,560 --> 00:00:44,490 be applied on cities, which has trend and seasonality. 9 00:00:45,900 --> 00:00:52,110 So to handle Trinian's seasonality, we integrate are Almo model with the different method. 10 00:00:54,090 --> 00:00:54,980 The idea's simple. 11 00:00:56,370 --> 00:00:59,490 We will first remove trend using the defensing method. 12 00:01:01,710 --> 00:01:05,860 We may have to do defensing several times if we are trying to remove non-linear trend. 13 00:01:05,970 --> 00:01:14,670 Also, once we have been rendered, we apply auto regression on this new series to find the initial 14 00:01:14,670 --> 00:01:15,660 set of forecast. 15 00:01:17,660 --> 00:01:23,280 Using these initial forecast, we find it is a duals or forecast errors. 16 00:01:24,770 --> 00:01:27,350 Then we apply moving average method on these days. 17 00:01:27,350 --> 00:01:29,240 Dools to update our forecasts. 18 00:01:31,010 --> 00:01:34,180 In the end, we reintroduce the effect of trend. 19 00:01:34,580 --> 00:01:42,260 By doing these different thing that is adding Bagdadi lag values to our forecast to include deterrent 20 00:01:42,260 --> 00:01:42,590 effect. 21 00:01:45,320 --> 00:01:51,650 Now, when we will be applying Atima in our software, we will have to specify three parameters. 22 00:01:53,300 --> 00:01:58,970 These three are parameters, letter to air, that is auto regression, ie. 23 00:01:59,570 --> 00:02:00,710 That is integration. 24 00:02:01,490 --> 00:02:03,500 And Emmy, that is moving average. 25 00:02:04,130 --> 00:02:07,700 These are denoted by B, B and Q. 26 00:02:10,070 --> 00:02:12,280 The first parameter is B. 27 00:02:13,580 --> 00:02:16,850 It is also called the order of auto regression. 28 00:02:18,320 --> 00:02:24,290 This basically denotes the number of blagged values we are going to use in our auto regression model. 29 00:02:26,180 --> 00:02:34,580 If we are using only P and T minus one values to forecast P plus one values, then B is equal to two. 30 00:02:35,900 --> 00:02:39,020 If we use only P value, then B is equal to one. 31 00:02:41,240 --> 00:02:42,660 Second parameter is D.. 32 00:02:43,640 --> 00:02:45,770 This is the order of different thing. 33 00:02:47,450 --> 00:02:53,160 This will tell our model how many times different thing is to be done to remove trend. 34 00:02:54,200 --> 00:03:01,720 If we are seeing a quadratic print, we may do defensing ways, in which case B will be equal due to 35 00:03:03,520 --> 00:03:05,840 bodily nutrient single. 36 00:03:05,900 --> 00:03:08,000 Different thing is sufficient. 37 00:03:08,480 --> 00:03:09,980 In which case we have these equal do. 38 00:03:09,980 --> 00:03:16,760 One last parameter is Q which is called the order of moving average. 39 00:03:18,410 --> 00:03:25,950 This is basically the window size of the desert world that we will be continuing to forecast future 40 00:03:25,960 --> 00:03:32,870 residuals if we are forecasting residuals on the basis of last three residual values. 41 00:03:33,470 --> 00:03:34,750 Then you is equal to three. 42 00:03:35,720 --> 00:03:39,230 If we are using last two residual values, then Q is equal to two. 43 00:03:41,240 --> 00:03:49,400 So by specifying these three parameters, our software will know what exactly is to be done to implement 44 00:03:49,520 --> 00:03:49,860 Atima. 45 00:03:52,250 --> 00:03:56,120 But how do we know what are the right values of BD and Q? 46 00:03:58,430 --> 00:04:03,680 One way to know is by using our experience and understanding of the problem at hand. 47 00:04:05,330 --> 00:04:13,010 For example, if we know that our product shows a quadratic print, we can straightaway said B is equal 48 00:04:13,010 --> 00:04:13,600 to two. 49 00:04:15,680 --> 00:04:24,080 The second way is by running a software iteration and finding which set of values of B, B and Q give 50 00:04:24,200 --> 00:04:26,210 the maximum estimate accuracy. 51 00:04:28,220 --> 00:04:34,970 When we do not have large data and there are limited number of options for BD and Q, this can also 52 00:04:34,970 --> 00:04:35,290 be done. 53 00:04:38,230 --> 00:04:39,530 So that's all about Rhema. 54 00:04:40,660 --> 00:04:42,730 See you in the political class of Atima.