ISYE 6501 FINAL PAPER COMPLETE
QUESTIONS AND ANSWERS
◉ Exponential Smoothing Pros and Cons.
Answer: Pros: Easy to learn and apply
Can produce accurate forecasts
can account for trends/seasonality/cyclic effects
Works well when mean/variance/etc are expected to remain
relatively constant
Cons: Forecasts can sometimes have lag
◉ ARIMA (Auto Regressive Integrated Moving Average).
Answer: A time series analysis method used for forecasting that
combines three components: Differences in differences to find
stationary change when data metrics aren't stationary.
Autoregression, where predicting current value is based on previous
time period values
Moving averages where we go back and incorporate q time periods'
previous errors
◉ GARCH (Generalized Autoregressive Conditional
Heteroskedasticity).
, Answer: Time series analytic method that estimates/forecasts
variance. Helps determine how much a forecast may be higher or
lower than the true value. Useful for estimating risks on investment
portfolios.
◉ Linear Regression.
Answer: A Regression technique that describes relationships
between independent and dependent variables as linear functions
◉ AIC (Akaike information criterion).
Answer: Model selection technique that balances model fit and
complexity. Penalizes models with too much complexity in an
attempt to avoid overfitting.
◉ BIC (Bayesian information criterion).
Answer: Model election technique that balances model fit and
complexity. Generally penalizes complexity more than AIC.
◉ Box-Cox Transformation.
Answer: Logarithmic transformation technique used to eliminate
heteroskedasticity (unequal variance) across a data set to make it fit
a normal distribution. This will help a model avoid being biased
towards parts of the data with greater variance.
QUESTIONS AND ANSWERS
◉ Exponential Smoothing Pros and Cons.
Answer: Pros: Easy to learn and apply
Can produce accurate forecasts
can account for trends/seasonality/cyclic effects
Works well when mean/variance/etc are expected to remain
relatively constant
Cons: Forecasts can sometimes have lag
◉ ARIMA (Auto Regressive Integrated Moving Average).
Answer: A time series analysis method used for forecasting that
combines three components: Differences in differences to find
stationary change when data metrics aren't stationary.
Autoregression, where predicting current value is based on previous
time period values
Moving averages where we go back and incorporate q time periods'
previous errors
◉ GARCH (Generalized Autoregressive Conditional
Heteroskedasticity).
, Answer: Time series analytic method that estimates/forecasts
variance. Helps determine how much a forecast may be higher or
lower than the true value. Useful for estimating risks on investment
portfolios.
◉ Linear Regression.
Answer: A Regression technique that describes relationships
between independent and dependent variables as linear functions
◉ AIC (Akaike information criterion).
Answer: Model selection technique that balances model fit and
complexity. Penalizes models with too much complexity in an
attempt to avoid overfitting.
◉ BIC (Bayesian information criterion).
Answer: Model election technique that balances model fit and
complexity. Generally penalizes complexity more than AIC.
◉ Box-Cox Transformation.
Answer: Logarithmic transformation technique used to eliminate
heteroskedasticity (unequal variance) across a data set to make it fit
a normal distribution. This will help a model avoid being biased
towards parts of the data with greater variance.
