HTM 4454 Lecture Notes - Lecture 9: Leap Years, Effective Demand

• School: Virginia Tech
• Department: Hospitality and Tourism Management
• Course: HTM 4454
• Professor: Perdue

Document Summary

Hb2 in nc, zika virus in miami: future data adjust for anticipated changes in the market and competitive environment c. i. Transportation system changes: qualitative/ feel adjustments beware of getting too caught up in the numbers and data, hotel data tracking, historical a. i. By day/ segment a. i. 1. a. i. 2. a. i. 3. a. i. 4. a. i. 5. By room type/ rate code: current data b. i. By day: future data, historical data forecasting, time series days sequence of days points, successive measurement over a time interval hourly, daily, weekly, etc, extrapolation models predicting future trends based on the past b. i. Assumption underlying factors causing change are constant: time series types c. i. Stationary- no significant upward of downward trend (no discernable trend) c. i. 1. Forecasting smoothing models to correct for noise. Non-stationary significant upward or downward trend c. ii. 1. Forecasting smoothing plus emphasis on trend: random walk / na ve forecasting extension, next period"s forecast equals this period"s actual, stationary data trend forecasting, simple moving average methods a. i.