Consider the following time series data. 1 2 3 4 5 6 Week Value 19 14 17 12 17 15 Calculate the measures of forecast error using the naive (most recent value) method and the average of historical data (to 2 decimals). Naive method Historical data Mean absolute error Mean squared error Mean absolute percentage error
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- Consider the following time series data: Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy: a. Mean absolute error b. Mean squared error c. Mean absolute percentage error d. What is the forecast for week 7?1. Consider the following time series data. Week 1 2 3 4 5 6 Value 18 13 17 11 18 15 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. (a) mean absolute error MAE, Mean squared error MSE, Mean absolute percentage error MAPE (b) What is the forecast for week 7?Consider the following time series data. Week 1 2 3 4 5 6 Value 18 14 16 10 19 13 Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. Mean absolute error. If required, round your answer to one decimal place. Mean squared error. If required, round your answer to one decimal place. Mean absolute percentage error. If required, round your intermediate calculations and final answer to two decimal places.
- Consider the following time series data. Week 1 2 3 4 5 6 Value 19 13 16 10 19 15 Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. (a) Mean absolute error If required, round your answer to one decimal place. (b) Mean squared error If required, round your answer to one decimal place. (c) Mean absolute percentage error If required, round your intermediate calculations and final answer to two decimal places. (d) What is the forecast for week 7?Consider the following time series data. Week 1 2 3 4 5 6 Value 19 14 15 10 17 15 Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. Mean absolute error. If required, round your answer to one decimal place.fill in the blank 1 Mean squared error. If required, round your answer to one decimal place.fill in the blank 2 Mean absolute percentage error. If required, round your intermediate calculations and final answer to two decimal places.fill in the blank 3% What is the forecast for week 7? If required, round your answer to two decimal place.Consider the following time series data. Week 1 2 3 4 5 6 Value 19 12 15 10 17 13 Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. Mean absolute error. If required, round your answer to one decimal place.fill in the blank 1 Mean squared error. If required, round your answer to one decimal place.fill in the blank 2 Mean absolute percentage error. If required, round your intermediate calculations and final answer to two decimal places.fill in the blank 3% What is the forecast for week 7? If required, round your answer to two decimal place.fill in the blank 4
- Consider the following time series data. Week 1 2 3 4 5 6 Value 20 12 16 11 18 14 Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. Given MAE = 5.6, MSE = 34 Mean absolute percentage error. If required, round your intermediate calculations and final answer to two decimal places. What is the forecast for week 7? If required, round your answer to two decimal place.Consider the following time series data. Week 1 2 3 4 5 6 Value 18 14 17 12 18 14 Calculate the measures of forecast error using the naive (most recent value) method and the average of historical data (to 2 decimals). Naive method Historical data Mean absolute error Mean squared error Mean absolute percentage error Which method provides the most accurate forecasts?Which of the following time series forecasting methods would not be used to forecast seasonal data?