HW4 – ISYE6501x: Exponential Smoothing & Forecasting

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  (the first smoothing parameter) to be closer to 0 or 1, and why?

Forecast of transportation users. This is always an important issue for every city. Transportation includes buses, subways, taxis, and recent emerging rideshares. In the case of taxis, it is necessary to predict how much taxi demand will occur according to the trend of taxi users and determine the number of permits issued accordingly. Taxi ride data is required for this experiment. Specifically, the time and location of the taxi ride are the most important, and additionally, road data may be desirable. Taxi demand has trends and seasonal factors. Therefore, the triple exponential smoothing method should be applied. This analysis requires accurate estimates of taxi demand, so the results should be as close as possible to the actual values. Thus, the alpha value for this will be close to one.

Using the 20 years of daily high-temperature data for Atlanta (July through October) from Question 6.2 (file temps.txt), build and use an exponential smoothing model to help make a judgment of whether the unofficial end of summer has gotten later over the 20 years. (Part of the point of this assignment is for you to think about how you might use exponential smoothing to answer this question. Feel free to combine it with other models if you’d like to. There’s certainly more than one reasonable approach.)

I first applied various alpha values and compared the results to see the difference of the estimated value according to the change of alpha value. Then, the seasonal parameter values were added with additive and multiplicative values, and the results were checked for differences. Based on the model, predictions were made for the next two years(h=246), and further predictions were performed using ARIMA.