Time series data generated by intelligent devices are growing rapidly and faced with serious data quality problems. The demand for time series data quality management and data quality improvement based on data repairing techniques is increasingly urgent. Time series data has the obvious characteristics about the ordered time window and strong associations between rows and columns. This brings much more challenges for the research of the data quality semantic expression of time series data. This study proposes the definition and the construction of time series data quality rules, which takes into account the association on both rows and columns. It extends the expression of the existing data quality rule systems in terms of time window and multi-order expression operation. In addition, the discovery method is proposed for time series data quality rules. Experiment results on real time series data sets verify that the proposed method can effectively and efficiently discover hidden data quality rules from time series data, showing that the proposed method has higher performance with the predicate construction of associated information on row and column, compared with the existing data quality rule discovery method.