From the course: Modeling Market Prices Using Stochastic Processes with Wolfram Language
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Time series model fit - Wolfram Language Tutorial
From the course: Modeling Market Prices Using Stochastic Processes with Wolfram Language
Time series model fit
So TimeSeriesModelFit is more or less what it says it is. It says if you've got some type of data, I'm going to fit a time series model to it. Now you can specify the model in advance. There's a shorthand notation for this. For instance, this would be for an ARMA, Autoregressive Moving Average, or you can let it determine it itself. In this particular case, it comes back and it's an ARIMA, Autoregressive Integrated Moving Average. Okay. So there's only one order of difference that's taking place here. Now once I've got this estimated process, I can then use it to forecast and I'm going to forecast four weeks ahead. Remember, our data here is weekly data. So I can then do that and I get a forecast, which in itself is another TemporalData object. And then I can join the two together, the time series and the temporal data and see what type of estimation it's giving us. So here's the previous history and there's the future that it's predicting. So as you can see, this is a very simple…
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