Write a program that will use the exponential smoothing to help you predict a value in the future. In addition your program should come up with the linear regression equation to predict the same value as was done in exponential smoothing. please find the full describtion of this assignment in attachment
Write a program that will use the exponential smoothing to help you predict a value in the future. In addition your program should come up with the linear regression equation to predict the same valu
Project: Exponential Smoothing Many time series values give spiked results This is difficult to develop a forecast on due to t he erratic nature. Exponential smoothing can smoot h this out so predictions are more stable: Where alpha = 0.25 Or perhaps this model: Where alpha = 0.5 Or perhaps this model: Where alpha = 0.75 What do you believe will happen if alpha is equal t o 1? Formula for Exponential Modeling: 1 1 t t 1 1 : note 1) and 0 (between constant smoothing t period for time forecast value series time 1 t period for time forecast ) 1( Y F F Y F where F Y F t t t t = = = = + = + = + + In this project your program will use the exponenti al smoothing to help you predict a value in the future. In addition your program should come up with the li near regression equation to predict the same value as was done in exponential smoothing. Part 1: Obtain the stock price inform from www.nasdaq.com . In this part, go to www.nasdaq.com , choose a company to analyze, click the “historical quotes” link on the left side after picking a company. Pick the stock prices for at least 8 months picking one data point out of each month as close to the f irst of the month as possible. The x values will be fro m 1 to 8 (where 1 indicates the first month looked at) while the y values will be the stock price. For ex ample: Would give the data point: x = 1, y = 187.21. For the second data point the list is scrolled to find to find the first historical quote for the next month: Would give the data point: x = 2, y = 176.02. And so on. Keep in mind that the values for x (1, 2, …, 8) DO NOT have to correspond to Jan, Feb, etc. x = 1 merely indicates the first month that you decided t o analyze. From there the months should proceed sequ entially. Once the data is gathered then you should have a list of values such as this example: Time period 1 2 3 4 5 6 7 8 Value 31.25 32.35 34.15 33.12 37.25 30.19 42.13 44.17 This table should be presented in an Excel document explaining why the choice was made for the company chosen (why would a model based on the firs t of the month possible make sense) and the time frame. Part 2: Write a Python program that asks the user f or the information from part 1 and performs exponential smoothing based on it. The perfect pro gram will allow the user to input alpha, display the graph of the original data and the “smoothed data” and have the user verify if this model is appropriate. If it is not then it should loop asking for new ent ries for alpha until the user indicates the model i s appropriate. At this point, it should use the expo nential smoothing model to predict time period 9 (x = 9). Read the explanation above closely to understa nd what exponential smoothing provides for the next month based on the previous month. Part 3: In the same Python program, the information from NASDAQ should be used to develop a linear regression model that is used to predict time perio d 9. It should show the correlation coefficient to indicate the strength of the model. No other tool is necessary for this project to test the appropriateness of using a linear regression model. Category 20 pts 15 pts 10 pts 5 pts 0 pts Data Collection Criteria – Company Identified and Choice Explained – Each data value obtained from first entry for the month chosen – Data was collect from sequential months – Data was delivered in an Excel spreadsheet All 4 criteria met Only 3 criteria was met Only 2 criteria was met Only 1 criteria was met None of the criteria was met Exponential Smoothing Logic Logic is 100% correct Logic is 75% correct Logic is 50% correct Logic is 25% correct Logic does not follow the exponential smoothing formula Visual Display of Smoothed Data Python program prints the original data and the smoothed data (not necessarily in the same graph) Python program does not print the graphs but writes the R program to a file which can then be opened into R and run. Python program does not print the graphs but outputs the appropriate R lines of code that can be Edit- Copied and Edit-Pasted into R for viewing for graph with no re- formatting necessary in R. Python program does not print the graphs but outputs the data that can be Edit- Copied and Edit-Pasted into R with manual reformatting of the data. (ex: 5, 8, 7, 10 Which then has to be formatted in R as x <- c(5,8,7,10) No attempt is made to display the graphs. Regression Logic Logic is 100% correct Logic is 75% correct Logic is 50% correct Logic is 25% correct Logic does not follow the regression logic from the last unit in the course. Structure and Design Criteria: - The appropriate flow mechanism is used in the program (while loop, etc.) for ease of use of the user - Code is placed in a library for code re-use - Code is documented where appropriate. - Code is “readable” (appropriate variable names and structured programmin g techniques used). All 4 criteria met Only 3 criteria was met Only 2 criteria was met Only 1 criteria was met None of the criteria was met Deliverables - Excel document with data and explanation of company choice - Python program - Python library (if a library is implemented) - R programs (any appropriate R programs)
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