Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Efficient in-database maintenance of ARIMA models
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Ad serving using a compact allocation plan
Proceedings of the 13th ACM Conference on Electronic Commerce
Sample-based forecasting exploiting hierarchical time series
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Partitioning and multi-core parallelization of multi-equation forecast models
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Forecasting user visits for online display advertising
Information Retrieval
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We propose a method for forecasting high-dimensional data (hundreds of attributes, trillions of attribute combinations) for a duration of several months. Our motivating application is guaranteed display advertising, a multi-billion dollar industry, whereby advertisers can buy targeted (high-dimensional) user visits from publishers many months or even years in advance. Forecasting high-dimensional data is challenging because of the many possible attribute combinations that need to be forecast. To address this issue, we propose a method whereby only a sub-set of attribute combinations are explicitly forecast and stored, while the other combinations are dynamically forecast on-the-fly using high-dimensional attribute correlation models. We evaluate various attribute correlation models, from simple models that assume the independence of attributes to more sophisticated sample-based models that fully capture the correlations in a high-dimensional space. Our evaluation using real-world display advertising data sets shows that fully capturing high-dimensional correlations leads to significant forecast accuracy gains. A variant of the proposed method has been implemented in the context of Yahoo!'s guaranteed display advertising system.