what did you think time series pattern exciti ? Trend ,seasonal ,horizontal ?
what did you think time series pattern exciti ? Trend ,seasonal ,horizontal ?
5. The components of time-series are: a. Trend, Seasonal, Movement, and Random b. Trend, Mobility, Cyclical, and Seasonal c. Trend, Seasonal, Cyclical, and Random d. Trend, Seasonal, Cyclical, and Perfection The mean absolute deviation measures the accuracy of a forecast by calculating.. a. the mid-point of absolute forecasting error per period of historical data. b. the average absolute forecasting error per period of historical data. c. the standard deviation of absolute forecasting error per period of historical data d. both...
quantitative
QUESTION 15 Given is a time series. What is the time series demand pattern as below? Date Price 6/26/2012 121.34 6/27/2012 102.56 6/28/2012 98.67 6/29/2012 99.6 7/2/2012 102.32 7/3/2012 95.23 7/5/2012 89.34 7/6/2012 82.37 79.56 7/10/2012 81.23 7/11/2012 72.67 7/12/2012 69.23 7/13/2012 62.85 7/16/2012 57.87 7/17/2012 58.23 7/9/2012 Horizontal Trend-Downward O Seasonal Trend and Seasonal Cyclical Trend-Upward com/webapps/assessment/take/launch.jsp?course_assessmen
Do you observe any seasonal pattern? What is it?
1800- 1600- Frig 1000- trend
Please explain your answer
Time Series and the Seasonal-Means+ Polynomial Trend 10 15 Time Figure 5 Which of the following characteristics is the model able to capture? Trend Seasonality Trend and seasonality Seasonality and heteroskedasticity
Discuss any apparent trend and seasonal pattern based on the case study and graph?
Question 7 (1 point) Saved Which of the following is predictable? Business cycles Trend Seasonal pattern Random variations
Please explain your answer
Suppose that we use least-squares to fit a seasonal-means trend to this time series. Figure 3 displays the original time series plot along wtih the fitted values. Time Series and Seasonal-Means Fit 10 15 Time Figure 3 Which of the following characteristics is the model able to capture? Trend Seasonality ● Trend and seasonality Seasonality and heteroskedasticity
The ratio-to-trend method is useful... (a) to capture the time trend (b) to adjust for seasonal variations (c) to capture random fluctuations (d) to control for secular cycles
3. Using the TGT Quarterly Sales (Target Corp.) data:Assume October 2011 is Quarter 3, Period (Trend) 1, etc.a. Fit a regression model with a time trend and seasonal dummy variables to the sales data.b. Is the time trend coefficient statistically significant? How can you tell?c. Are the seasonal dummy variables statistically significant? How can you tell?d. Assume time is 0. Calculate sales for Q3. Round to two decimal places.e.What is the coefficient on the first quarter? Round to two decimal...
If we fit a linear trend to 10 observations on time-series data that are growing exponentially, then it is most likely that: the fitted trend will be too high at t = 1 and t = 10. the fitted trend will be too low in the middle. the forecasts (if extrapolated) will be too low. the residuals will show a pattern like - - - + + + + - - -.