3 The following are the values of a time series for the first four time periods:...
Below you are given the first four values of a time series. Time Period Time Series Value 1 18 2 20 25 17 Using a fourth-order moving average, the forecasted value for period 5 is 2.5 20 O 17 10
The number of four-period centered moving averages of a time series with 20 time periods is: a 28 b 24 c 20 d 16
Problem II The following time series shows the sales of a clothing store over a 10-week period. Week Sales ($1,000s) 15 a. Compute a 4-week moving average for the above time series. b. Compute the mean square error (MSE) and mean Absolut deviation (MAD) for the 4. week moving average forecast. c. Use a -0.3 to compute the exponential smoothing values and MSE and MAD for the time series. d. Forecast sales for week 11. e. Which model is the...
The following is the data of recent refrigerator sales at a local Home Depot store. Month 1 2 3 4 5 Actual Sales 95 100 80 90 ??? Inputs will be exact numbers. What is the forecasted sales in month 5 using naive approach. Please use a 2-month simple moving average method to forecast sales in month 5. Please use a weighted moving average method, with weights of 0.6 one period ago, 0.3 two periods ago, and 0.1 three periods...
Develop three-month and four-month moving averages for this time series. (Round your answers to two decimal places.) Month Time Series Value 3-Month Moving Average Forecast 4-Month Moving Average Forecast 1 9.5 2 9.3 3 9.4 4 9.6 5 9.9 6 9.8 7 9.8 8 10.5 9 9.9 10 9.7 11 9.6 12 9.6 Does the three-month or four-month moving average provide more accurate forecasts based on MSE? Explain. The four-month moving average provides more accurate forecasts, because its MSE is...
The actual values for 12 periods (shown in order) are: (1) 45 (2) 52 (3) 48 (4) 59 (5) 55 (6) 55 (7) 64 (8) 58 (9) 73 (10) 66 (11) 69 (12) 74 Using a 5 period simple moving average, the forecast for period 13 will be: QUESTION 2 Using the 4 period weighted moving average, the forecast for period 13 will be: QUESTION 3 With exponential smoothing, the forecast for period 13 will be: QUESTION 4 With linear regression, the forecast for period 13 will be: QUESTION...
Check My Work (3 remaining Consider the following gasoline sales time series data. Click on the datafile logo to reference the data Week Sales (1000s of gallons) 20 18 17 19 21 12 a. Using a weight of for the most recent observation, for the second most recent observation, and third the most recent abaervation, compute a threa-week weightad moving avarage fos the time series (to 2 decimals). Enter nagative values as negative numbers Weighted Moving Average Forecast (Error Time-Series...
Please use Excel Solver. Consider the following set of time series sales data for a growing company over the past 8 months: Month Sales 1 15 2 13 18 4 22 20 6 23 7 22 8 21 1. Construct a time series plot. What type of pattern exists? 2. Develop a forecast for the next month using the averaging method. 3. Develop a forecast for the next month using the naïve last-value method. 4. Develop a forecast for the...
Exhibit 18-2 Consider the following time series. Refer to Exhibit 18-2. The slope of linear trend equation, b1, is Refer to Exhibit 18-2. The intercept, b0, is Refer to Exhibit 18-2. The forecast for period 5 is Refer to Exhibit 18-2. The forecast for period 10 is Please if you can show work so I can understand along with equations. I have the answers already and am just working to see how I got those answers. For the following time...
Different types of time-series forecasting models and their applicability in different organizations are given below: 1. Naive approach: In naive approach, demand for the next period is assumed to be same in the most recent period. This method can be used in economic and financial time series analysis. It can be used to forecast demand for mature products having level or seasonal demand without a trend. 2. Moving average: This method uses a number of historical data to determine the...