A forecasting method used to predict can opener sales applies the following set of weights to the last five periods of data: .1, .1, .2, .2, .4 (with .4 being applied to the most recent observation). Observed values of can opener sales arePeriod:                1     2     3      4     5     6      7      8Observation:     18   22   26    33   14   28    30    52Determine the following:a. The one-step-ahead forecast for period 9.b. The one-step-ahead forecast that was made for period 6.

Describe when to use of a time series forecasting techniques and what assumption are made?

A recently introduced product has a positive trend in sales. The product’s sales have increased in each of its first eight weeks on the market. The forecast for week 6 was 1,325 (F6 = 1,325), with A6 = 1,283 and T6 = 42. Using smoothing components α = 0.20 and β = 0.30, develop forecasts for weeks 7 and 8.

Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data: Week 6 weeks ago 5 weeks ago 4 weeks ago 3 weeks ago 2 weeks ago Last week # Students 83 110 95 80 65 50 What is this week's forecast using trend-adjusted (double) smoothing with alpha = .5 and beta = .1, if the forecast for last week was 65, the forecast for two weeks ago was 75, and the trend estimate for last week's forecast was -5? 49.3 78.7 51.3 50.6 65.4

How would you choose the appropriate number of factors to use in a forecasting model and how would you build a factor augmented VAR.

One forecasting model was used to forecast demand for a product. The forecasts and the demand are shown in the table below. Use two decimals in your answer. Actual Forecast 1 40 41 2 35 38 3 38 35 4 33 30 The Mean Absolute Deviation (MAD) is O 0.5 2.5 O 1.48 7.01

Here are the actual tabulated demands for an item for a nine-month period (January through September). Your supervisor wants to test two forecasting methods to see which method was better over this period.   MONTH ACTUAL January 120 February 145 March 146 April 171 May 154 June 182 July 138 August 135 September 146     a. Forecast April through September using a three-month moving average. b. Use simple exponential smoothing with an alpha of  0.20 to estimate April through September, using the average of January through March as the initial forecast for April.  c-1. Calculate MAD for Three-month moving average and Exponential smoothing. c-2. Use MAD to decide which method produced the better forecast over the six-month period.

The sales of Bluetooth Headphones at the Dubai Electronics Enterprises in Jebel Ali, UAE, over the past 4 months have been 100, 110, 120, and 130 units (with 130 being the most recent sales). Develop a moving-average forecast for next month, using the following techniques: 5B. If next month's sales turn out to be 140 units, forecast the following month's sales (months) using a 4-month moving average.

Asvnch Problem - Statistical Forecasting Data Set – Eunice BC Fashion Monthly Sales, in million units. Year Total Sales Year Total Sales 2010 38 2016 43 2011 41 2017 40 2012 40 2018 45 2013 45 2019 47 2014 50 2020 42 2015 42 2021 48 Questions: a. Find the naïve forecast. b. Use the 3 years moving average forecast. c. Have a 5 years weighted moving average. d. Develop forecast using exponential smoothing with a = 0.2. e. Determine the trend line equation and present the forecast. f. Find the best forecast for year 2022. Note: Use the first 5 years as the training samples and the last 5 years as the forecasting samples. Solve it in Excel Sheet/Sheet with Equations as possible.

The accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-26, where x = 0.5 and y = 0.8. Then find the MAD for periods 13-24. Click the icon to view the monthly use of natural gas data. Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26. (Type integers or decimals rounded to two decimal places as needed.) Forecast Period 13 14 15 16 17 18 Natural Gas Usage Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period Gas Usage 1 2 8699 A WI 3 4 5 7 9 10 11 12 13 14 1516718192021 22 23 24 241 221 155 137 54 34 29 27 30 40 90 201 229 238 242 136 34 34 28 26 27 40 86 187