Abstract
Amidst the avalanche of articles on big and machine learning, the phrase after cleaning the data is often found. Here we focus on the work hidden behind this phrase. We analyze the types of dirty found in financial time series, the problems caused by dirty data, and the performance of cleaning algorithms. And we extend the MSSA hole filling algorithm of Kondrashov and Ghil to improve its performance on CDS spread data, and combine it with clustering techniques from science to detect bad data.
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