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# Georgia Institute Of Technology ISYE 6501 Homework 4 Complete Solutions - Introduction To Analytics Modeling - GTX ISYE 6501

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ISYE 6501 OAN - Homework Week 4 Contents 1 Question 7.1 1 2 Question 7.2 2 2.1 Set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Load ... and examine the data for this assignment . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.3 Convert temps data to vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.4 Convert temps data to timeseries object . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.5 Test Model 1: single exponential smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.6 Test Model 2: double exponential smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.7 Test Model 3: triple exponential smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.8 Test Model 4: triple exponential smoothing with multiplicative . . . . . . . . . . . . . . . . . 9 2.9 Compare additive vs multiplicative triple exponential smoothing models . . . . . . . . . . . . 11 2.10 Extract seasonality from the models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.11 QCC on seasonality to determine if summer is later . . . . . . . . . . . . . . . . . . . . . . . . 14 2.12 R Cusum - No adjustments for T or C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.13 R Cusum - T equal 10, C = 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3 Testing different values of T and C using R cusum 16 3.1 R Cusum decision.interval = .925, se.shift = .374 . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 R Cusum decision.interval = 1.5, se.shift = .25 . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.3 R Cusum decision.interval = 1.96, se.shift = .3 . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1 Question 7.1 1.0.1 Describe a situation or problem from your job, everyday life, current events, etc., for which exponential smoothing would be appropriate. What data would you need? Would you expect the value of ff (the first smoothing parameter) to be closer to 0 or 1, and why? When I worked for the commuter rail system in Illinois (known as Metra) the organization kept detailed records on something known as “on-time” performance. On-time performance is a measurement of how often commuter trains arrive on-time at their destination. With over a thousand daily commuter rail trips, each trip consisting of 1 or more stops, there is a lot of data. The state agency responsible for operating the commuter trains uses the data to estimate on-time performance and adjust schedules for trains on a daily basis. Forecasting using expotential smoothing would likely increase the accuracy of on-time performance and may reveal weekly or seasonal trends, or day-part trends, that are hidden in the data. Valuable data would include date, time and on-time performance - likely measured as a variance from the planned arrival at train stations. I believe the first smoothing factor would be closer to 0 because I would expect more randomness in the data due to the large number of random events that can occur - anything from equipment issues or failures to passenger or public issues - that can impact on-time performance. [Show More]

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