compute statistics. Make visualization. Split the data for forecast validation. Define an LSTM. Forecast univariate time series with an LSTM.
Create a Jupyter Notebook in order to forecast a univariate time series (in our case new one family home sales) using an LSTM. You will also be able to tell when univariate time series have the appropriate structure to be forecasted with LSTM's or even using any other univariate forecasting techniques. This Guided Project was created by a Coursera community member.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Define and Estimate the LSTM.
Separating Data into Training and Validations Sets Format to Feed to LSTM
Introduction to the Project and the Instructor.
Load the data and Make Transformations.
Descriptive Statistics and Visualizations the Data.
Forecast the LSTM on the Validation Set and Assess Accuracy.
Autocorrelations and Partial Autocorrelations Plots.
Unit Root Test.
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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