AN 300 Final Exam Uky Question and
answers rated A+ 2025/2026
Time-Series forecasting - correct answer ✔- A time series is a record of sequential observations of an
item of interest over time (e.g., demand/sales).
- Time-series forecasting is a predictive analytics technique that uses historical values of data on an item
of interest to:
- Uncover underlying data patterns in sequential observation of an item of interest overtime
- Project the pattern into the future
- Predict the outcome of the item of interest
Time series forecast: 1. Level 2. Trend 3. seasonality
Data Patterns - correct answer ✔• Horizontal/level • Trend• Seasonality• Cycle , Random
Horizontal/Level, Trend, Seasonality, Cycle terms - correct answer ✔Horizontal/Level: A constant
average value over time.
Trend: A gradual upward or downward shift in values over time.
Seasonality: A recurring pattern that occurs at set periods within a larger time frame.
Cycle: An alternating pattern of points lying above and below an underlying pattern (as opposed to
random fluctuation) across multiple years.
Business application - correct answer ✔• Marketing- Generate sales forecasts of different brands of
products
for production planning. • Management
- Predict market growth rate for strategic planning.• Operations
- Generate product demand forecasts for material requirements planning.
, Naive Method - correct answer ✔Naïve forecast for the next period = Actual value of the last period.
- Require only one historical data value.
- Adapt more readily to sudden shifts in the data pattern.
- For data with any pattern
- Track recent movement of data
- Simple moving average of order 1
Naive Method example - correct answer ✔Naive forecast for July will be the actual demand for June
Smoothing methods - correct answer ✔For short-range (e.g. weekly) forecasts of time-series data with
a horizontal pattern.
- Need a series of historical data values and other method depends parameters
- Historical moving/Simple average
- Simple moving average of order K
- Exponential smoothing with alpha and a starting forecast
"Smooth" out the random component of the time series. Slow to adapt to sudden shifts in the
underlying data pattern.
Historical moving average - correct answer ✔Historical moving average forecast for the next period =
Average of all prior actual historical data.
- The average of all prior actual data points in the time series.
- Also known as simple average method.
- Requires no other input data except the data points in the time series.
Historical mining average method example - correct answer ✔The average demand "January to June"
for all months divided by the amount of months = average
answers rated A+ 2025/2026
Time-Series forecasting - correct answer ✔- A time series is a record of sequential observations of an
item of interest over time (e.g., demand/sales).
- Time-series forecasting is a predictive analytics technique that uses historical values of data on an item
of interest to:
- Uncover underlying data patterns in sequential observation of an item of interest overtime
- Project the pattern into the future
- Predict the outcome of the item of interest
Time series forecast: 1. Level 2. Trend 3. seasonality
Data Patterns - correct answer ✔• Horizontal/level • Trend• Seasonality• Cycle , Random
Horizontal/Level, Trend, Seasonality, Cycle terms - correct answer ✔Horizontal/Level: A constant
average value over time.
Trend: A gradual upward or downward shift in values over time.
Seasonality: A recurring pattern that occurs at set periods within a larger time frame.
Cycle: An alternating pattern of points lying above and below an underlying pattern (as opposed to
random fluctuation) across multiple years.
Business application - correct answer ✔• Marketing- Generate sales forecasts of different brands of
products
for production planning. • Management
- Predict market growth rate for strategic planning.• Operations
- Generate product demand forecasts for material requirements planning.
, Naive Method - correct answer ✔Naïve forecast for the next period = Actual value of the last period.
- Require only one historical data value.
- Adapt more readily to sudden shifts in the data pattern.
- For data with any pattern
- Track recent movement of data
- Simple moving average of order 1
Naive Method example - correct answer ✔Naive forecast for July will be the actual demand for June
Smoothing methods - correct answer ✔For short-range (e.g. weekly) forecasts of time-series data with
a horizontal pattern.
- Need a series of historical data values and other method depends parameters
- Historical moving/Simple average
- Simple moving average of order K
- Exponential smoothing with alpha and a starting forecast
"Smooth" out the random component of the time series. Slow to adapt to sudden shifts in the
underlying data pattern.
Historical moving average - correct answer ✔Historical moving average forecast for the next period =
Average of all prior actual historical data.
- The average of all prior actual data points in the time series.
- Also known as simple average method.
- Requires no other input data except the data points in the time series.
Historical mining average method example - correct answer ✔The average demand "January to June"
for all months divided by the amount of months = average
