Akaike Information Criterion (AIC) is one of the most commonly used model selection criteria, calculated as:
AIC=T ln (residual sum of squares)+ 2n
where n=number of parameters estimated (p + q+ possible constant term), T= number of usable observations. Ideally, smaller values of the AIC are preferred (note that the value can be negative). AIC is used to aid in selecting the most appropriate model; model A is said to fit better than model B is the AIC is smaller than that for model B.
Average Daily Rate (commonly referred to as ADR, or average room rate) is a statistical unit that is often used in the lodging industry. The ADR can be calculated by dividing the room revenue by the number of rooms sold.
Consumer price index (CPI) is a measure of the overall cost of the goods and services bought by a typical consumer.
Cross-price elasticity of demand (CED) is a measure of how much the quantity demanded of one good responds to a change in the price of another good, computed as the percentage change in quantity demanded of the first good (A) divided by the percentage change in the price of the second good (B).
CED=
Cross-sectional data are data on one or more variables collected at the same point in time, such as the data collected from consumer expenditure surveys in Mainland China, Hong Kong and Taiwan.
Delphi method is a systematic, interactive forecasting method which relies on a panel of independent experts. The carefully selected experts answer questionnaires in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts’ forecasts from the previous round as well as the reasons they provided for their judgments. Thus, experts are encouraged to revise their earlier answers in light of the replies of other members of their panel. It is believed that during this process the range of the answers will decrease and the group will converge towards the "correct" answer. Finally, the process is stopped after a pre-defined stop criterion (e.g. number of rounds, achievement of consensus, stability of results) and the mean or median scores of the final rounds determine the results.
Demand Forecast Error is the deviation of the actual realized demand quantity from the forecasted quantity. The Forecast error can be bigger than actual or forecast but NOT both. Error above 100% implies a zero forecast accuracy or a very inaccurate forecast.
Error (%) = (Actual-Forecast) / Demand
Accuracy (%) = 1-Error (%)
Elasticity is a measure of the responsiveness of quantity demanded or quantity supplied to one of its determinants.
Endogenous (or, dependent) variable is a variable in an equation whose value is to be determined by the solution of the equation depends on the values of other (the independent) variables.
Exogenous (or, independent) variable is a variable which is presumed to affect or determine a dependent variable. It can be changed as required, and its values do not represent a problem requiring explanation in an analysis, but are taken simply as given.
Explanatory variable is used in a relationship to explain or to predict changes in the values of another variable; the latter is called the dependent variable.
Forecast error is the difference between the actual and forecast values of a forecasting model which could be defined by
Gross domestic product (GDP) is the market value of all final goods and services produced within a country in a given period of time.
Income elasticity of demand (IED) is a measure of how much the quantity demanded of a good responds to a change in consumers’ income, computed as the percentage change in quantity demanded divided by the percentage change in income.
IED= Percentage change in quantity demanded/ Percentage change in income
Inflation rate is the percentage change in the price index from the preceding period. The inflation rate between two consecutive years is computed as follows:
Inflation rate in year 2 = 
Judgmental forecasting methods incorporate intuitive judgments, opinions and probability estimates, as in the case of the Delphi method, scenario building, and simulations.
Mean absolute percentage error (also known as MAPE) is measure of accuracy in a fitted time series value in statistics, specifically trending. It usually expresses accuracy as a percentage.
MAPE=
The difference between actual value At and the forecast value Ft, is divided by the actual value At again. The absolute value of this calculation is summed for every fitted or forecast point in time and divided again by the number of fitted points n. This makes it a percentage error so one can compare the error of fitted time series that differ in level.
Mean squared error or MSE of an estimator is one of many ways to quantify the amount by which an estimator differs from the true value of the quantity being estimated. As a loss function, MSE is called squared error loss. MSE measures the average of the square of the "error." The error is the amount by which the estimator differs from the quantity to be estimated. The difference occurs because of randomness or because the estimator doesn't account for information that could produce a more accurate estimate.
Nominal GDP is the production of goods and services valued at current prices.
Pool data combine both time series and cross-sectional components, which are also called panel or longitudinal data. The information on cross-sectional units (say, households, firms or tourism receiving/generating countries) in this type of data is observed over time.
Price elasticity of demand (PED) is a measure of how much the quantity demanded of a good responds to a change in the price of that good, computed as the percentage change in quantity demanded divided by the percentage change in price. Demand for a good is said to be elastic if the quantity demanded responds substantially to changes in the price. Demand is said to be inelastic if the quantity demanded responds only slightly to changes in the price. It could be computed as:
PED =
Real GDP is the production of goods and services valued at constant prices. To calculate real GDP, you need to first choose one year as a base year and then use the prices of specified goods/service in the base year to compute the value of goods and services in all of the years. In other words, the prices in the base year provide the basis for comparing quantities in different years.
Root mean square deviation (RMSD) or root mean square error (RMSE) is a frequently-used measure of the differences between values predicted by a model or an estimator and the values actually observed from the thing being modeled or estimated. The RMSD of an estimator with respect to the estimated parameter 
is defined as the square root of the mean squared error:
RMSD()=
=
Schwartz Baysian Criterion (SBC) is one of the most commonly used model selection criteria, calculated as:
SBC=T ln(residual sum of squares)+n ln(T)
where n=number of parameters estimated (p+ q+ possible constant term), T= number of usable observations. Ideally, smaller values of the AIC are preferred (note that the value can be negative). SBC is used to aid in selecting the most appropriate model; model A is said to fit better than model B is the AIC is smaller than that for model B.
Stochastic process is said to be stationary if its mean and variance are constant over time and the value of covariance between two time periods depends only on the distance or lag between the two time periods and not on the actual time at which the covariance is computed.
Time series data is a sequence of observations taken sequentially in time. Many sets of data appear as time series: monthly hotel occupancy rate, quarterly arrivals or annual tourism expenditure.
Time series analysis comprises methods that attempt to understand such time series, often either to understand the underlying context of the data points (Where did they come from? What generated them?), or to make forecasts (predictions).