To benefit from these ML models, time series data have to be transformed into (labeled) feature vectors.

Data are often sparse in time, non-stationary, carry seasonality pattern and trends. A frequent requirement for time series techniques is that the data be stationary. This argument holds for the time series models supported here as well. This includes aggregation, resampling, interpolation to fill missing values and more. Time series data often carry seasonality pattern and trends and are non-stationary.

This project shows how to easily integrate Apache Spark, a fast and general purpose engine for large-scale data processing, with Elasticsearch, a real-time distributed search and analytics engine. Spark is an in-memory processing framework and outperforms Hadoop up to a factor of 100. If you are more interested in an Elasticsearch plugin-in that brings the power of Predictiveworks. Predictiveworks. is an ensemble of dedicated predictive engines that covers a wide range of today's analytics requirements from Association Analysis, to Context-Aware Recommendations up to Text Analysis. Besides linguistic and semantic enrichment, for data in a search index there is an increasing demand to apply knowledge discovery and data mining techniques, and even predictive analytics to gain deeper insights into the data and further increase their business value.