In this assignment, you will obtain time series data from your workplace or from the Internet (if workplace data is not available). The data must be current. That is, the most recent observation must be no more than six months old. The time series must be valid and have at least 50 observations. Once you have obtained the data, you will analyze the time series and then create a number of forecasts. Then, based on the mean absolute deviation, you will choose the best forecasting model to make a new forecast. You will use MS Excel for all of your analysis. Note: Teams may not share data sets. I should receive unique data sets and analysis from each team. Requirements: 1. In one or two paragraphs, briefly describe the source of the data and the type of business. Cite your sources in text and include them on your work cited page. See the Style Guide (on course website) for instructions. 2. Create a line chart and identify the time series components in the time series. Justify your answer. (Hint: it should be some combination of average or base, cycle, trend, seasonality, and random variation.) 3. Create the following forecasts using as much of the data as possible: a. 5-period moving average, a 7-period moving average, and a 12-period moving average. b. Exponential smoothing forecasts with alpha = 0.2, alpha = 0.5, and alpha = 0.9. C. 4-period weighted moving average, a 7-period weighted moving average, and a 12- period weighted moving average. Remember that you are not required to follow the textbook's restriction on weights. All that is required is that the weights are between 0 and 1 and that they all sum to 1. FYI: since I have required you to create 7 and 12-period WMAs, it is possible to model some weekly and monthly cycles (seasonality) by choosing the appropriate weights. d. Trend forecast (whether or not there is a trend). Use the TREND() function in Excel to generate the trend forecast. 4. Calculate the MAD for each forecasting model (there are a total of ten models listed above), and choose the best model based on this analysis. 5. Using the best model, make a new forecast for the next period. This is important. Include it in your report! 6. Based on your analysis and the time series data context (e.g, sales of X or shipments from Y), do you believe that the forecast is reliable enough to base a business decision on? Why or why not? You must defend your answer. If you use workplace data, then your insight should be based on e: Teams may not share data sets. I should receive unique data sets and analysis from each team. uirements: 1. In one or two paragraphs, briefly describe the source of the data and the type of business. Cite your sources in text and include them on your work cited page. See the Style Guide (on course website) for instructions. 2. Create a line chart and identify the time series components in the time series. Justify your answer. (Hint: it should be some combination of average or base, cycle, trend, seasonality, and random variation.) 3. Create the following forecasts using as much of the data as possible: a. 5-period moving average, a 7-period moving average, and a 12-period moving average. b. Exponential smoothing forecasts with alpha = 0,2, alpha = 0.5, and alpha = 0.9. c. 4-period weighted moving average, a 7-period weighted moving average, and a 12- period weighted moving average. Remember that you are not required to follow the textbook's restriction on weights. All that is required is that the weights are between 0 and 1 and that they all sum to 1. FYI: since I have required you to create 7 and 12-period WMAs, it is possible to model some weekly and monthly cycles (seasonality) by choosing the appropriate weights. d. Trend forecast (whether or not there is a trend). Use the TREND() function in Excel to generate the trend forecast. 4. Calculate the MAD for each forecasting model (there are a total of ten models listed above), and choose the best model based on this analysis. 5. Using the best model, make a new forecast for the next period. This is important. Include it in your report! 6. Based on your analysis and the time series data context (e.g., sales of X or shipments from Y), do you believe that the forecast is reliable enough to base a business decision on? Why or why not? You must defend your answer! If you use workplace data, then your insight should be based on your business knowledge. If you use Internet data, then you must conduct additional research to make an educated assessment of forecast quality. Note: Knowing what actually happened is not a good way to do this. Remember that in this exercise, we are assuming that you do not know the future. Good answers consider whether the forecast makes sense, and they look at the magnitude of the error as measured by MAD. In this assignment, you will obtain time series data from your workplace or from the Internet (if workplace data is not available). The data must be current. That is, the most recent observation must be no more than six months old. The time series must be valid and have at least 50 observations. Once you have obtained the data, you will analyze the time series and then create a number of forecasts. Then, based on the mean absolute deviation, you will choose the best forecasting model to make a new forecast. You will use MS Excel for all of your analysis. Note: Teams may not share data sets. I should receive unique data sets and analysis from each team. Requirements: 1. In one or two paragraphs, briefly describe the source of the data and the type of business. Cite your sources in text and include them on your work cited page. See the Style Guide (on course website) for instructions. 2. Create a line chart and identify the time series components in the time series. Justify your answer. (Hint: it should be some combination of average or base, cycle, trend, seasonality, and random variation.) 3. Create the following forecasts using as much of the data as possible: a. 5-period moving average, a 7-period moving average, and a 12-period moving average. b. Exponential smoothing forecasts with alpha = 0.2, alpha = 0.5, and alpha = 0.9. C. 4-period weighted moving average, a 7-period weighted moving average, and a 12- period weighted moving average. Remember that you are not required to follow the textbook's restriction on weights. All that is required is that the weights are between 0 and 1 and that they all sum to 1. FYI: since I have required you to create 7 and 12-period WMAs, it is possible to model some weekly and monthly cycles (seasonality) by choosing the appropriate weights. d. Trend forecast (whether or not there is a trend). Use the TREND() function in Excel to generate the trend forecast. 4. Calculate the MAD for each forecasting model (there are a total of ten models listed above), and choose the best model based on this analysis. 5. Using the best model, make a new forecast for the next period. This is important. Include it in your report! 6. Based on your analysis and the time series data context (e.g, sales of X or shipments from Y), do you believe that the forecast is reliable enough to base a business decision on? Why or why not? You must defend your answer. If you use workplace data, then your insight should be based on e: Teams may not share data sets. I should receive unique data sets and analysis from each team. uirements: 1. In one or two paragraphs, briefly describe the source of the data and the type of business. Cite your sources in text and include them on your work cited page. See the Style Guide (on course website) for instructions. 2. Create a line chart and identify the time series components in the time series. Justify your answer. (Hint: it should be some combination of average or base, cycle, trend, seasonality, and random variation.) 3. Create the following forecasts using as much of the data as possible: a. 5-period moving average, a 7-period moving average, and a 12-period moving average. b. Exponential smoothing forecasts with alpha = 0,2, alpha = 0.5, and alpha = 0.9. c. 4-period weighted moving average, a 7-period weighted moving average, and a 12- period weighted moving average. Remember that you are not required to follow the textbook's restriction on weights. All that is required is that the weights are between 0 and 1 and that they all sum to 1. FYI: since I have required you to create 7 and 12-period WMAs, it is possible to model some weekly and monthly cycles (seasonality) by choosing the appropriate weights. d. Trend forecast (whether or not there is a trend). Use the TREND() function in Excel to generate the trend forecast. 4. Calculate the MAD for each forecasting model (there are a total of ten models listed above), and choose the best model based on this analysis. 5. Using the best model, make a new forecast for the next period. This is important. Include it in your report! 6. Based on your analysis and the time series data context (e.g., sales of X or shipments from Y), do you believe that the forecast is reliable enough to base a business decision on? Why or why not? You must defend your answer! If you use workplace data, then your insight should be based on your business knowledge. If you use Internet data, then you must conduct additional research to make an educated assessment of forecast quality. Note: Knowing what actually happened is not a good way to do this. Remember that in this exercise, we are assuming that you do not know the future. Good answers consider whether the forecast makes sense, and they look at the magnitude of the error as measured by MAD