Although rapid development of information technologies in the past decade has provided forecasters with both huge amounts of data and massive computing capabilities, these advancements do not necessarily translate into better forecasts. Different industries and products have unique demand patterns. There is not yet a one-size-fits-all forecasting model or technique. A good forecasting model must be tailored to the data in order to capture the salient features and satisfy the business needs. This paper presents a multistage modeling strategy for demand forecasting. The proposed strategy, which has no restrictions on the forecasting techniques or models, provides a general framework for building a forecasting system in multiple stages.

We use SAS^® Forecast Studio to develop time series models for the daily number of taxi trips and the daily taxi fare revenue of Yellow Cabs in New York City, using publicly available data from 1/1/2011 to 6/30/2015. Interest centers on trying to assess the effects (if any) of the Uber ride-hailing service on Yellow Cabs in New York City.

Many organizations need to report forecasts of large numbers of time series at various levels of aggregation. Numerous model-based forecasts that are statistically generated at the lowest level of aggregation need to be combined to form an aggregate forecast that is not required to follow a fixed hierarchy. The forecasts need to be dynamically aggregated according to any subset of the time series, such as from a query. This paper proposes a technique for large-scale automatic forecast aggregation and uses SAS^® Forecast Server and SAS/ETS^® software to demonstrate this technique.

SAS^® Forecast Server provides an excellent tool towards forecasting of time series across a variety of scenarios and industries. Given sufficient data, SAS Forecast Server can provide insights into seasonality, trend, and effects of input variables and events. In some business cases, there might not be sufficient data within individual series to produce these complex models. This paper presents an approach to forecasting these short time series. Using sufficiently long time series as a training data set, forecasted shape and volumes are created through clustering and attribute-based regressions, allowing for new series to have forecasts based on their characteristics. These forecasts are passed into SAS Forecast Server through preprocessing. Production of artificial history and input variables, matched with a custom model addition to the standard model repository, allows for future values of the artificial variable to be realized as the forecast. This process not only relieves the need for manual entry of overrides, but also allows SAS Forecast Server to choose alternative models as additional observations are provided through incremental loads.

The importance of understanding terrorism in the United States assumed heightened prominence in the wake of the coordinated attacks of September 11, 2001. Yet, surprisingly little is known about the frequency of attacks that might happen in the future and the factors that lead to a successful terrorist attack. This research is aimed at forecasting the frequency of attacks per annum in the United States and determining the factors that contribute to an attack's success. Using the data acquired from the Global Terrorism Database (GTD), we tested our hypothesis by examining the frequency of attacks per annum in the United States from 1972 to 2014 using SAS^® Enterprise Miner™ and SAS^® Forecast Studio. The data set has 2,683 observations. Our forecasting model predicts that there could be as many as 16 attacks every year for the next 4 years. From our study of factors that contribute to the success of a terrorist attack, we discovered that attack type, weapons used in the attack, place of attack, and type of target play pivotal roles in determining the success of a terrorist attack. Results reveal that the government might be successful in averting assassination attempts but might fail to prevent armed assaults and facilities or infrastructure attacks. Therefore, additional security might be required for important facilities in order to prevent further attacks from being successful. Results further reveal that it is possible to reduce the forecasted number of attacks by raising defense spending and by putting an end to the raging war in the Middle East. We are currently working on gathering data to do in-depth analysis at the monthly and quarterly level.