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- Which of the following time series forecasting methods would not be used to forecast seasonal data?1. Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.The following data set provides the total number of shipments of core major household appliances in the U.S. from 2000 to 2016 (in millions): Year Shipments (millions) 2000 38.4 2001 38.2 2002 40.8 2003 42.5 2004 46.1 2005 47.0 2006 46.7 2007 44.1 2008 39.8 2009 36.5 2010 38.2 2011 36.0 2012 35.8 2013 39.2 2014 41.5 2015 42.9 2016 44.7 a. Plot the time series. b. Fit a three-year moving average to the data and plot the results. c. Fit a five-year moving average to the data and plot the results. d. Compute a linear trend forecasting equation and plot the trend line. e. Compute a quadratic trend forecasting equation and plot the results.
- Given the number of incidents between 1 to 7 October 2021 in the table below: Date Incidents 1/10/2021 99430 2/10/2021 101786 3/10/2021 103843 4/10/2021 106318 5/10/2021 109804 6/10/2021 113381 7/10/2021 116864 Calculate the MAPE only for these 7 days and compare the forecasting accuracy of the model for this period with its fitting accuracy during the observation period.Consider the following time series data. a. Show the four-quarter and centered moving average values for this time series (to 3 decimals if necessary). b. Compute seasonal indexes and adjusted seasonal indexes for the four quarters (to 3 decimals).consider the following time series data.Month 1 2 3 4 5 6 7Value 24 13 20 12 19 23 15a. compute MSe using the most recent value as the forecast for the next period. Whatis the forecast for month 8?b. compute MSe using the average of all the data available as the forecast for the nextperiod. What is the forecast for month 8?c. Which method appears to provide the better forecast?
- #4) Commuter ridership in Athens, Greece, during the summer months is believed to be heavily tied to the number of tourists visiting the city. During the past 12 years, the data are given in the following table. Year Number of Tourists (millions) Ridership (hundreds of thousands) 1 6 11 2 11 16 3 8 16 4 10 14 5 19 28 6 18 26 7 16 21 8 20 25 9 24 45 10 18 28 11 11 18 12 19 35 a) Create a time series plot for the ridership. b) Using linear regression to see if using the year is a good predictor for the ridership. What is the regression equation? How accurate is the model? c) Using linear regression to see if using the number of tourists is a good predictor for the ridership. What is the regression equation? How accurate is the model? d) Which linear regression equation is better? What is the expected ridership if 10 million tourists visit the city next year? e) Excel FileConsider the following gasoline sales time series data. Click on the datafile logo to reference the data. Week Sales (1000s of gallons) 1 16 2 20 3 20 4 23 5 18 6 17 8 19 9 23 10 19 11 14 12 21 a. Using a weight of 1/2 for the most recent observation, 1/3 for the second most recent observation, and 1/6 the most recent observation, compute a three-week weighted moving average for the time series (to 2 decimals). Enter negative values as negative numbers. Week Time-Series Value Weighted MovingAverage Forecast ForecastError (Error)2 1 2 3 4 5 6 7 8 9 10 11 12 Total b. Compute the MSE for the weighted moving average in part (a).MSE = Do you prefer this weighted moving average to the unweighted moving average? Remember that…The following ratio-to-moving averages for the seasonally adjusted series were found by the decomposition method applied on a time series representing quarterly sales for January 2018 to December 2020 period: a. Calculate the Seasonal Index for every quarter. b. If the trend is described by the trend line T^ = 1,000 + 30 t, what is the forecast for the fourth quarter of 2021?
- Which of the time series forecasting methods would not be used to forecast seasonal data?For the following time series plots, explain what type of transformation, if any, would make the variance more stable.Consider the following gasoline sales time series data. Click on the datafile logo to reference the data. Week. Sales (1000s of gallons) 1 16 2. 22 3. 18 4. 23 5. 18 6. 17 7 21 8 17 9 21 10. 21 11 14 12 23 Using a weight of 1/2 for the most recent observation, 1/3 for the second most recent observation, and third the most recent observation, 1/6 compute a three-week weighted moving average for the time series (to 2 decimals). Enter negative values as negative numbers. Week Time-Series Value Weighted MovingAverage Forecast ForecastError (Error)2 Total b.…