FinTech 10-3 Time Series

What were the objectives of Time Series day 3?

• Define overfitting and articulate the benefits of parsimony

• Evaluate the accuracy of out-of-sample predictions

• Create a time series linear regression model using Scikit-learn

• Create seasonal clustering analysis using Scikit-learn

• Analyze and predict seasonal effects using regression

Which are components of a linear regression model?

• Slope

• Error

• Y-intercept

• Dependent variable

• Independent variable

Which letter in the linear model is the independent variable?  $y\ =\ mx\ +b$  

• x

• y

• m

• b

Which are metrics can be used to check goodness of fit for a regression model?

• r-square value

• mean squared error

• root mean squared error

• correlation coefficient

What does this result show?

• Low bias, low variance

• High bias, low variance

• Low bias, high variance

• High bias, high variance

Which image shows overfitting?

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What do we call the simplest, good-fitting model?

• Parsimonious

• Significant

• Autoregressed

• Linear

How was Time Series Day 3 for you?

Any suggestions for improvement?