Exam 20: Time-Series Analytics and Forecasting

It must be understood that the ____________________ of a forecast decreases rapidly for predictions more than one time period into the future.

The Pyramids of Giza The Pyramids of Giza is one of the most visited monuments in Egypt.The number of visitors per quarter has been recorded (in thousands)as shown in the accompanying table: ​ ​ -{The Pyramids of Giza Narrative} Use the seasonal indexes and the linear trend calculated in the previous questions to forecast the number of visitors in the next four quarters and describe the seasonal fluctuations in the number of visitors.

A time series is shown in the table below. a.Plot the time series.Would the linear or quadratic model fit better? b.Use the regression technique to calculate the linear trend line and the quadratic trend line.Which model fits better?

Two forecasting models were used to predict the future values of a time series.These are shown in the accompanying table,together with the actual values. Compute the mean absolute deviation (MAD)and sum of squares for forecast (SSE)for each model to determine which was more accurate.

The quarterly earnings of a large microcomputer company have been recorded for the years 2011-2014.These data (in millions of dollars)are shown in the accompanying table. Use an appropriate moving average to measure the quarterly variation by computing the seasonal (quarterly)indexes.

A least squares ____________________ trend line is just a simple regression line with the years recoded.

The following are the values of a time series for the first four time periods: Using a four-period moving average,the forecasted value for time period 5 is:

The Pyramids of Giza The Pyramids of Giza is one of the most visited monuments in Egypt.The number of visitors per quarter has been recorded (in thousands)as shown in the accompanying table: ​ ​ -{The Pyramids of Giza Narrative} Discuss why exponential smoothing is not recommended as a forecasting method in this case.

The results of a quadratic model fit to time series data were = 8.5 − 0.25t + 2.5t²,where t = 1 for 1998.The forecasted value for 2005 is 129.25.

If a time series does not exhibit a long-term trend,the method of exponential smoothing may be used to obtain short-term predictions about the future.

The equation: S_t = w⋅y_t + (1 −w)⋅St− ₁ (for t≥ 2)refers to exponentially smoothed time series.

Daily Hoagie Sales The table below shows the number of hoagies sold daily during a four-week period at Hoagie Haven in Sutton,West Virginia. ​ ​ -{Daily Hoagie Sales Narrative} Calculate the seasonal (daily)indexes,using a seven-day moving average.

Daily Sandwich Sales The daily sales figures shown below have been recorded in a sandwich shop. ​ ​ -{Daily Sandwich Sales Narrative} Compute the three-day and five-day moving averages.

Liquor Sales The number of cases of liquor sold by a liquor wholesaler in an 8-year period follows. ​ ​ -{Liquor Sales Narrative} Exponential smoothing with a weight or smoothing constant of 0.2 will be used to smooth the liquor sales.The smoothed value for 2012 is_________________.

Weekly iPhones sales (in $1,000s)in an Apple store for the past three months are shown in the table below.Compute the four-week centered moving averages.

Hotel Occupancy A small hotel has recorded the number of rooms occupied during weekdays over a period of four weeks as is shown in the table below: -{Hotel Occupancy Narrative} Calculate the seasonal indexes,based on the regression trend line developed in the previous question.

Forecasts based on trend and seasonality are generated by:

Biodiesel Sales Biodiesel (a vegetable oil or animal fat based diesel fuel)sales in Nebraska have been recorded over the past 10 months as shown below. ​ ​ -{Biodiesel Sales Narrative} Compute the five-month moving average.

Coffee Imports The coffee imports (in millions of dollars)from a Latin American country for 10 years are shown below. ​ ​ -{Coffee Imports Narrative} Calculate the percentage of trend.

The trend line = 125 + 2t and seasonal indexes shown in the table below were computed from 10 years of quarterly data.Forecast the values for the next four quarters.