Time is an amazing factor. A little baby can grow up overtime. The weather will change overtime. Stock prices can change overtime as well. Is very important for us to understand the relationships among data values overtime series. We want to learn how to use analytical model to analyse data overtime. From a mathematical standpoint, we can present time source data in three ways. The first one is called autoregressive model.
The second one is called a moving average model.
The third way is a combination of Auto Regressive and Moving Average models. The autoregressive model believe that Rome was not built in one day. The data value at the current time spot is built on top of the data values at the previous time spots. For example. Today’s stock price is built on top of the stock prices in previous days. If we use the autoregressive model to analyse a time series data. Our job is to identify how many days we need to look back in order to forecast the current value. What should be the value for coefficients like beta 0, beta one, beta 2 and so on? This is autoregressive model. In practise, we really use a short name AR to represent autoregressive model. The second model is called moving average model. This model below that. The current data value is a result from previous I expected events. For example, the current stock price can be a result of I expected events like EU S election results. The Cover 19 pandemic. A Better sales revenue. The CEO change of the company and so. If we use the moving average model to analyse time series data. Our job is to identify how many days of I expected events. We want to look back to focus the current value. What should be the values for the coefficients like theatre one, theatre 2 and so on? In practise, we really use a short name MA to represent moving average model. The third one is a combination of a RNMA model. This model believes that both AR model and MA model have a valid point. So why don’t we combine both models together in order to forecast the current value? In practise, we use a short name ARMA to represent the autoregressive moving average model. No matter which model you plan to choose. The first step is to make sure that I’m serious data follows a stationary data assumption. The concert’s data sets should have a constant mean and a constant variance. The constant mean and variance should be the same throughout the entire time series. No matter where you choose the time period. In practise, when we are given a time series data set. The very first step is to draw the time Series A plot charts. If a time series follows the stationary date assumption. The time series should be pretty flat. The data value should change around a central value. As you can see in the charts in the top charts of this light. If the time series is not stationary data sets. Then you should have an obvious trend either decreasing or increasing in the blood chart. One way face non stationary data sets. What can we do to transform this non stationary data sets into a stationary data set? In practise, we use a technique called a different thing to do the data transformation. Simply speaking, differences mean that we want to subtract previous values from the current value.. Can you see where we use full leg difference in and twelve leg difference in? Yes, we can use them to process signal data. We need to transform seasonality into stationary data set as well. In the next few lectures, I will talk about all of these techniques in more details. Fortunately, we don’t have to do difference in my hands. As long as you figure out what types of difference in you want to do. How many times you want to do the difference in?
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