Auto Regression | Time Series Talk : Autoregressive Model


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Time Series Talk : Autoregressive Model


In this video, we’re going to be continuing our exploration into timeseriesforecasting and we’ll be talking about one of my all-time favorite models, the AR model or the auto regressive model. Let’s just talk about the name for a second before we get into this really easy example auto regressive, so that means that it’s a regression that you’re probably familiar with right. You’re trying to predict something based on other things, but this is a specific type of regression. It’s an auto regression, which means you’re trying to predict something based on past values of that same thing, and that’s a really powerful point that I think doesn’t get emphasized enough in timeseriesforecasting videos or courses is that it’s very natural to want to predict something. Maybe it’s the price of some kind of item, or it’s the quantity of something you need or it’s. The number of houses sold per month. Whatever it is, of course, there’s a lot of factors going into each thing, such as the weather or the stock market or many other different things, but what’s more natural than saying? I want to predict the value of that thing today. Based on what the value of that thing was yesterday, based on what the value of that thing was last week last month last year. Going back right because that thing’s gonna change in maybe some particular way maybe it’s not been predictable at all, but chances are that there could be some pattern that emerges and if we can capture that pattern, we can get a much stronger prediction, especially if we incorporate all those more common things that people think of when you do a regression. All these other factors, okay so. I wanted to just give you guys a really really gentle introduction into why auto regression is a very powerful concept. Now let’s get into the example and how you would figure out. What is the best auto regressive model for your situation? So in this setup, you are a milk salesman More, particularly. You are a distributor of milk. You ship milk all over the country and one really big problem for you is month by month. You want to know how much milk should? I produce so that I can have the exact amount for pretty much the right amount to ship to everyone who needs it. I don’t want to have too much right, because I don’t have milk, which is going to spoil. I don’t want to have too little because then I can’t fulfill all my orders, so you want to know exactly? How much milk should I load onto the truck this month? So let’s say you go ahead and see if you can use timeseriesforecasting or an auto regressive model. Maybe for this kind of situation, so the first thing you do Is you go ahead and drop a plot where the Y-axis is the quantity of milk that is shipped and the x-axis is time so here we’re saying each of these blocks separated by the purple dotted lines are years, so here’s 2016 2017 and 2018 And you make a chart of how much milk was demanded in each of those months. So each of these black dots, here’s a month. Maybe let’s say and you draw it out. You can already see a pretty clear pattern here, right as you go into the month into the year. The quantity of milk demanded, goes up up up and a little bit more halfway past a given year. Then it dips right, and then maybe a plateaus and then at the beginning of the next year, it starts all over again up and then down up and then down so this is a very predictable pattern that you can take advantage of to predict exactly how much milk you might need for any given month in the future in 2019 and young. Now, how would we figure that out? How would we figure out? Let me introduce some notation here so we can write a model in just a second. Let’s say M Sub T is the quantity of milk that is demanded this month. Let’s say M sub T minus 1 is the quantity of milk that was demanded last month, so minus 1 and and Ts minus 12 for example, is the quantity of milk That was demanded 12 months ago or this time last year. Okay, so this is our notation for quantity of milk demanded, of course, the thing. I’m trying to predict is M sub T because I’m in my current time period and the thing I have available to predict with or all these And so T minus 1 minus 2 minus 12 however much. I want, however, much data. I actually have right so one naive approach you could say, hey, why don’t? I just throw every single lag from 1 through 12 maybe into the model then. I’ll have a great prediction model, right, because I’m incorporating all the data that I have. Well, you might get a seemingly strong model, but it’s gonna be prone to a lot of statistical issues like overfitting, which just means that it’s too too tuned to your certain data and besides in statistics in regression modeling if a simpler model can do the job or pretty much same job as a very complicated model, we’re going to prefer that simple model because it’s going to hold up better over time. So for that reason, we want to figure out only which lags only which of these T – what are important for our situation we’re going to be using our good friend. The PA CF chart or partial autocorrelation function. So if you haven’t seen my video on autocorrelation and partial autocorrelation go ahead and watch that if you really don’t want to watch it, then the basics of Pa CF are that the PA CF at a given lab. So for example, PA, CF of lag 1 is going to be the direct correlation. Actually, maybe better to say the. P AC F of 3 It’s going to be the direct correlation of the quantity of note demanded three months ago on the quantity of note today without considering so removing the effects of the intermediary. Temporaries, which are so we’re trying to do. Mt – three direct effect on M Sub T. That means it removes the effect of M sub T – – price of the quarter Damon up two months ago and M Sub T minus one quantity of milk Just last night. It’s the direct effect, So it’s pretty natural here. We only want to keep the lags whose direct effects are high in magnitude, either positive or negative, if those direct effects are zero or statistically very close to zero. We don’t want to include those lives because if some certain lab has no direct correlation with our quantity of milk donated today. Why would we include it? It’s not important, it’s just going to make our model noisy and cluttered, right, so we only want to include the lands whose PA CF are above these red bands and these red bands. Basically, you can think of them. As anything. Within the red bands we don’t, we think is statistically close to zero anything outside. The red bands are statistically different than zero, so we have evidence to say that anything else Other advanced is actually different from zero, so let’s just go through our target and see lag. One definitely is statistically different than zero in a positive direction lag – statistically different from zero in the negative direction, like three does not cut it because it’s below the top air band lag for does cut it statistically different from zero in the negative direction. And let’s say all these lags in between, do not cut it, but the lag at twelve or one year ago well months ago does cut it and it’s very strong. Okay, and let’s just say that all the lags after twelve are statistically below zero, they don’t cut it, so we’re only concerned with these four that do cut it. Okay, so what might a good model look like a good model might look like, Of course, we first start out with the thing we’re trying to predict, which is M Sub T. We have a coefficient here. They debate or not the intercept, and then we have beta one. And, of course, the first flag is M sub T minus 1 plus Beta 2 and sub T minus 2 Then 3 didn’t cut it, so we have 4 plus beta for M Sub T minus 4 and then we had one more theta 12 and sub T minus. Bob and we need to include that error term. So me box this model in a different color purple here. So this based on our evidence could be a good model to help us predict the quantity of milk demanded today, based on the quantity of milk demanded a month ago two months ago four months ago and 12 months ago. Okay, and we deduced that based on the PA CF plot, which again is just measuring the direct correlation, the price of milk some number of lives ago along the price. I’m sorry quantity of milk some months ago on the quantity of milk today that is the basics of an AR model And the reason I liked it. So much is just its simplicity. Its simplicity, starting from the concept of it, predicting something based on past values of that thing to figuring out a model based on this p. ACF, which is very intuitive to think about going from there to actually creating your model and testing, okay. This was a very gentle introduction to a our models. Of course, there’s many other factors going into this, but we will save those for in a future video. Okay, so until next time.

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