1 00:00:01,350 --> 00:00:04,140 How far in the future should we forecast? 2 00:00:06,180 --> 00:00:10,200 Should we generate all the forecasts at a single time point? 3 00:00:11,020 --> 00:00:14,250 Or can we forecast on an ongoing basis? 4 00:00:16,350 --> 00:00:20,190 These are important questions to be answered at the gold definition stage. 5 00:00:21,900 --> 00:00:28,860 But before we try to find these answers, let us discuss some basic notation that we are going to use. 6 00:00:28,950 --> 00:00:29,340 And this. 7 00:00:31,270 --> 00:00:40,590 We are going to use four types of symbols to denote time period data as forecasts and forecast errors. 8 00:00:42,900 --> 00:00:46,170 So B will be used to denote the time period. 9 00:00:47,830 --> 00:00:54,950 This is equal to one is the first time period indices and these equal to N means d end it. 10 00:00:55,050 --> 00:01:04,040 Time period, the whity will be used to denote the actual value of the variable of interest in the pit 11 00:01:04,170 --> 00:01:04,740 time period. 12 00:01:06,420 --> 00:01:14,820 For example, if we start measuring daily average temperatures at the start of the week, then these 13 00:01:14,820 --> 00:01:22,980 equal to one denotes day one or Monday B is equal to two, denotes day two or Tuesday and so on. 14 00:01:24,420 --> 00:01:28,900 And Vivan denote the average temperature of Monday. 15 00:01:30,240 --> 00:01:30,690 And why? 16 00:01:30,690 --> 00:01:33,840 To denote the average temperature of Tuesday and so on. 17 00:01:36,240 --> 00:01:37,340 Next is F.T.. 18 00:01:38,130 --> 00:01:41,180 This will be the forecasted value for time period. 19 00:01:42,990 --> 00:01:44,100 Not the actual value. 20 00:01:44,450 --> 00:01:46,320 Actual value is denoted by whity. 21 00:01:47,160 --> 00:01:49,850 Forecasted value will be denoted by F.T.. 22 00:01:51,360 --> 00:01:59,050 Similarly, F.T. plus K will then denote D gay step ahead forecast when the forecasting time. 23 00:02:01,200 --> 00:02:08,160 If we are currently a time Beretti, the forecast for the next time period that is D plus one is denoted 24 00:02:08,160 --> 00:02:09,600 by F people as one. 25 00:02:10,230 --> 00:02:17,280 So if you forecast the average temperature of Wednesday on a Monday, you are forecasting for peoples 26 00:02:17,310 --> 00:02:18,600 tuart time period. 27 00:02:22,050 --> 00:02:25,940 Lastly, E.T. is the forecast error for time purity. 28 00:02:27,570 --> 00:02:32,480 That is it is the difference between the actual value and defocused value at 90. 29 00:02:33,870 --> 00:02:38,460 So mathematically, it will be equal to Viotti minus F.T.. 30 00:02:40,200 --> 00:02:43,530 This is basically the inaccuracy of our forecast. 31 00:02:46,350 --> 00:02:54,570 With this notation, let us denote the forecast horizon with key basically gauge the number of time 32 00:02:54,570 --> 00:02:57,900 period ahead that we will be forecasting. 33 00:02:58,950 --> 00:03:05,430 For example, if Gaitskell equal three four monthly data, this means that we will be forecasting for 34 00:03:05,430 --> 00:03:09,550 the next three months in our Amtrak ridership. 35 00:03:09,570 --> 00:03:17,970 Example, one month Ed forecast, that is if people's money will be sufficient for revenue management. 36 00:03:18,420 --> 00:03:20,520 That is for flexible pricing. 37 00:03:22,320 --> 00:03:32,520 Whereas longer term forecast such as dreman three months ahead forecast or F, P plus three are more 38 00:03:32,520 --> 00:03:36,300 likely to be needed for procurement and inventory purposes. 39 00:03:37,980 --> 00:03:42,150 Knowing forecast horizon, as does an important part of goal setting. 40 00:03:43,710 --> 00:03:47,830 One of the factors that affect forecast horizon is the decency of data available. 41 00:03:49,050 --> 00:03:55,360 Forecasting next month's ridership is much harder if we do not have the data for last two months. 42 00:03:56,910 --> 00:04:01,740 If we do not have the data for last two months, it actually means that we are generating forecasts 43 00:04:02,550 --> 00:04:06,210 of F.P. plus three rather than F.T. plus one. 44 00:04:09,850 --> 00:04:18,250 Now, what is the impact of large or small forecast horizon on forecasting accuracy the further into 45 00:04:18,250 --> 00:04:19,000 the future? 46 00:04:19,120 --> 00:04:26,140 We are trying to forecast the more likely that the forecasting context will change and certainty will 47 00:04:26,140 --> 00:04:26,680 increase. 48 00:04:27,340 --> 00:04:36,940 Therefore, for larger reason, we can expect large uncertainty and less accuracy for such a situation. 49 00:04:37,480 --> 00:04:45,640 We should probably examine our model and try and updated, as in when new data or information is available. 50 00:04:48,510 --> 00:04:57,270 For example, if we have a three month ahead forecast for April of 2020, which we have generated in 51 00:04:57,330 --> 00:05:06,110 January 2020, if possible, we should update this forecast in February and again in March of this year. 52 00:05:09,610 --> 00:05:14,080 Lastly, we will discuss two points, forecasting use and level of automation. 53 00:05:16,060 --> 00:05:17,920 How will the forecast be used? 54 00:05:18,910 --> 00:05:26,080 Understanding how forecasts will be used, perhaps by different stakeholders is critical for generating 55 00:05:26,080 --> 00:05:27,600 forecasts of the right type. 56 00:05:28,060 --> 00:05:32,980 And with a useful accuracy limit, consider this question. 57 00:05:33,580 --> 00:05:39,250 Does over prediction cost more or it cost less than the under prediction? 58 00:05:40,540 --> 00:05:46,510 For example, if you're organizing an event and you want to forecast how many people will attend it, 59 00:05:48,610 --> 00:05:56,200 if you all predict and arrange for 120 people instead of hundreds who actually turn up, there will 60 00:05:56,200 --> 00:05:58,780 be some cost incurred for 20 empty seats. 61 00:06:00,460 --> 00:06:07,270 On the other hand, if you under predict and arrange for 80 people only, you will lose revenue of 20 62 00:06:07,270 --> 00:06:07,720 people. 63 00:06:08,350 --> 00:06:14,500 And there will be some emotional loss also for those 20 people who came but were not allowed to enter. 64 00:06:16,570 --> 00:06:23,620 Similarly, this question will be forecast and forecasting method is to be presented to management or 65 00:06:23,710 --> 00:06:30,940 to the technical department, answer to this question will tell us the level of complexity in our model 66 00:06:31,030 --> 00:06:31,960 that we can use. 67 00:06:32,740 --> 00:06:36,430 Similarly, we ask, should the forecast be numerical or binary? 68 00:06:37,660 --> 00:06:44,740 For example, if you're forecasting whether it will rain or not tomorrow, it is a binary forecast. 69 00:06:46,660 --> 00:06:51,820 On the other hand, if you are forecasting the average temperature for the next day, that is a numerical 70 00:06:51,820 --> 00:06:52,360 forecast. 71 00:06:54,040 --> 00:06:56,530 Different models work for these two types of problems. 72 00:06:57,640 --> 00:07:03,940 We will cover both types of models then, of course, but you must know which one is to be applied for 73 00:07:03,940 --> 00:07:04,900 the problem at hand. 74 00:07:06,100 --> 00:07:11,950 Next is a set of questions to determine the level of automation required in the forecasting project. 75 00:07:14,170 --> 00:07:20,170 The level of required automation depends on the nature of forecasting tasks and on how the forecast 76 00:07:20,290 --> 00:07:21,460 will be used in practice. 77 00:07:23,140 --> 00:07:27,690 For example, when we ask how many cities need to be forecasted. 78 00:07:28,240 --> 00:07:35,800 If for us in Adio there are many cities which are do we forecasted and not much forecasting expertise 79 00:07:35,800 --> 00:07:36,820 in the organization. 80 00:07:37,210 --> 00:07:38,890 We must automate the solution. 81 00:07:40,780 --> 00:07:48,430 A classic example is forecasting at the point of sale for purpose of inventory control across many stores. 82 00:07:50,530 --> 00:07:55,540 Organizations do not depend on these salesperson depuis us to manage inventory. 83 00:07:56,910 --> 00:07:59,290 It is, it added, managed automatically by the system. 84 00:08:00,610 --> 00:08:03,640 Several consulting firms provide such software solutions. 85 00:08:07,040 --> 00:08:11,910 The question is, is default casting an ongoing process or a one time event? 86 00:08:13,020 --> 00:08:16,530 If it is a one time event, we need not implement automation. 87 00:08:17,820 --> 00:08:22,800 We also might want to know which daytimes software will be available during the forecasting period. 88 00:08:24,150 --> 00:08:30,600 Word forecasting expertise will be available at the organization during forecasting period because lack 89 00:08:30,600 --> 00:08:32,610 of expertise favours automation. 90 00:08:33,390 --> 00:08:39,900 So if we do not have a lot of expertise in the organization, we may want to automate the forecasting 91 00:08:39,900 --> 00:08:43,950 process so that it does not depend on deep people in the organization. 92 00:08:45,900 --> 00:08:53,190 So you can see different answers will lead to different choices of data forecasting methods and evolution 93 00:08:53,190 --> 00:08:53,790 schemes. 94 00:08:55,980 --> 00:09:00,990 And all these questions must be answered at the gold definition stage.