Market analysis is a representative data analysis process with many applications. In such an analysis, critical numerical measures, such as profit and sales, fluctuate over time and form timeseries data. Moreover, the time series data correspond to market segments, which are described by a set of attributes, such as age, gender, education, income level, and product-category, that form a multi-dimensional structure. To better understand market dynamics and predict future trends, it is crucial to study the dynamics of time-series in multi-dimensional market segments. This is a topic that has been largely ignored in time series and data cube research. In this study, we examine the issues of anomaly detection in multi-dimensional time-series data. We propose timeseries data cube to capture the multidimensional space formed by the attribute structure. This facilitates the detection of anomalies based on expected values derived from higher level, \more general” time-series. Anomaly detection in a time-series data cube poses computational challenges, especially for high-dimensional, large data sets. To this end, we also propose an efficient search algorithm to iteratively select subspaces in the original high-dimensional space and detect anomalies within each one. Our experiments with both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed solution. Mining Approximate Top K Subspace Anomalies in MultiDimensional TimeSeries Data