Hitting the memory wall: implications of the obvious
ACM SIGARCH Computer Architecture News
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A skip-list approach for efficiently processing forecasting queries
Proceedings of the VLDB Endowment
FAST: fast architecture sensitive tree search on modern CPUs and GPUs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Forecasting high-dimensional data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
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Forecasting is an important analysis technique used in many application domains such as electricity management, sales and retail and, traffic predictions. The employed statistical models already provide very accurate predictions, but recent developments in these domains pose new requirements on the calculation speed of the forecast models. Especially, the often used multi-equation models tend to be very complex and their estimation is very time consuming. To still allow the use of these highly accurate forecast models, it is necessary to improve the data processing capabilities of the involved data management systems. For this purpose, we introduce a partitioning approach for multi-equation forecast models that considers the specific data access pattern of these models to optimize the data storage and memory access. With the help of our approach we avoid the redundant reading of unnecessary values and improve the utilization of the CPU cache. Furthermore, we utilize the capabilities of modern multi-core hardware and parallelize the model estimation. Our experimental results on real-world data show speedups of up to 73x for the initial model estimation. Thus, our partitioning and parallelization approach significantly increases the efficiency of multi-equation models.