Deep Learning - Recurrent Neural Networks with TensorFlow - Other Ways to Forecast

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Computers
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11th - 12th Grade
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a potential downside of using a multi-step forecast iteratively?
It can lead to overly optimistic results.
It always provides the most accurate predictions.
It may result in numerical precision errors.
It is too simple for complex problems.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it important to benchmark your forecasting model against a baseline?
To ensure your model is the most complex.
To verify that your model is not underperforming compared to a simpler model.
To make your model more complicated.
To avoid using any baseline models.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a naive forecast?
A forecast that uses future data for predictions.
A forecast that predicts the last observed value.
A forecast that predicts the average of past values.
A forecast that uses complex algorithms.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In which scenario is a naive forecast considered the best approach?
When dealing with a random walk model.
When predicting complex time series data.
When forecasting multiple steps ahead.
When using neural networks.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How can neural networks be adapted for multi-step forecasting?
By using only one input feature.
By specifying multiple output nodes for each time step.
By increasing the number of hidden layers.
By using a single output node.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a common mistake people make when presenting forecasting results?
Claiming to predict the future when only predicting one step ahead.
Using too simple models.
Ignoring the baseline model.
Predicting too many steps ahead.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of a baseline in time series analysis?
To ensure predictions are always accurate.
To complicate the forecasting process.
To provide a simple reference point for model performance.
To eliminate the need for complex models.
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