Abstract: A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.

Abstract: A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.

Abstract: A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce forecasts. The sequence of operations include model strategy operations for applying various model strategies to the time series to determine error distributions corresponding to the model strategies. The sequence of operations further include a model-strategy comparison operation for determining which of the model strategies is a champion model strategy for the plurality of time series based on the error distributions of the model strategies. The pipeline is executed to determine the champion model strategy for the time series.

Abstract: A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce forecasts. The sequence of operations include model strategy operations for applying various model strategies to the time series to determine error distributions corresponding to the model strategies. The sequence of operations further include a model-strategy comparison operation for determining which of the model strategies is a champion model strategy for the plurality of time series based on the error distributions of the model strategies. The pipeline is executed to determine the champion model strategy for the time series.

Abstract: Implementations described and claimed herein provide systems and methods for migration data from a source to a target in the background using an optimal number of threads. In one implementation, a file system operation request is received at the target. The file system operation request specifying a read request for a directory having a directory level migration attribute on the target that is marked. An optimal number of threads is allocated for migrating the directory. Metadata for content in the directory is obtained from the source using the optimal number of threads. A directory entry for a file in the directory is created on the target using the metadata, the directory entry for the file associated with a file level migration attribute that is marked.

Abstract: Implementations described and claimed herein provide systems and methods for migration data from a source to a target in the background using an optimal number of threads. In one implementation, a directory entry in a source file system is compared to a size threshold. An optimal number of threads for a migration associated with the directory entry is allocated. The optimal number of threads is determined based on a degree of parallelism available for the migration and the comparison of the directory entry to the size threshold. The directory entry is migrated from the source file system to a target file system using the optimal number of threads.

Abstract: Implementations described and claimed herein provide systems and methods for migration data from a source to a target in the background using an optimal number of threads. In one implementation, a file system operation request is received at the target. The file system operation request specifying a read request for a directory having a directory level migration attribute on the target that is marked. An optimal number of threads is allocated for migrating the directory. Metadata for content in the directory is obtained from the source using the optimal number of threads. A directory entry for a file in the directory is created on the target using the metadata, the directory entry for the file associated with a file level migration attribute that is marked.

Abstract: Implementations described and claimed herein provide systems and methods for migration data from a source to a target in the background using an optimal number of threads. In one implementation, a directory entry in a source file system is compared to a size threshold. An optimal number of threads for a migration associated with the directory entry is allocated. The optimal number of threads is determined based on a degree of parallelism available for the migration and the comparison of the directory entry to the size threshold. The directory entry is migrated from the source file system to a target file system using the optimal number of threads.