From 5d968387826fc7824b346c4d0ca3c69941e39679 Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Sat, 29 May 2021 23:15:39 -0400 Subject: [PATCH] Update README.md --- TimeSeries/2-ARIMA/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/TimeSeries/2-ARIMA/README.md b/TimeSeries/2-ARIMA/README.md index aebdaaedc..a839c05f9 100644 --- a/TimeSeries/2-ARIMA/README.md +++ b/TimeSeries/2-ARIMA/README.md @@ -184,7 +184,7 @@ results = model.fit() print(results.summary()) ``` -TODO: Explain these results and show residuals +A table of results is printed. You've built your first model! Now we need to find a way to evaluate it. @@ -345,11 +345,13 @@ plt.show() ``` A very nice plot, showing a model with good accuracy. Well done! + ## 🚀Challenge Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE in this lesson, but are there other methods you could use? Research them and annotate them. A helpful document can be found [here](https://otexts.com/fpp2/accuracy.html) ## [Post-lecture quiz](link-to-quiz-app) + ## Review & Self Study This lesson touches on only the basics of Time Series Forecasting with ARIMA. Take some time to deepen your knowledge by digging into [this repository](https://microsoft.github.io/forecasting/) and its various model types to learn other ways to build Time Series models.