Multi-dimensional Time Series Analysis VS OLAP iunera

Multi-dimensional Time Series Analysis and OLAP methods are important, when working with Time Series Data. Often multi-dimensional Time Series Analysis as term is referred to is a complete set of methods in applying machine learning in forms of forecasts or searching for anomalies and patterns. In this article we focus on good old deterministic multi-dimensional Time Series Analysis foundations to prepare, investigate and aggregate the Time Series Data in a deterministic way. Knowing these multi-dimensional Time Series Analysis foundations is essential, because at least 80% of Data Science work is Big Data and Big Data Landscape preparation. Common multi-dimensional analysis operations get applied in Business Intelligence and Data Warehousing where they are often called Online AnaLytical Processing (OLAP) operations [1]. In this article, we discuss and describe what the most important multi-dimensional Time Series Analysis and OLAP methods are and show examples of how the different operations are applied on a Time Series Data sets. In the beginning, we talk about OLAP in Data Warehouse landscapes and Time Series Data processing in Big Data landscapes. Subsequently, we give some insights into why and to whom multi-dimensional time series analysis with OLAP matters within an enterprise.