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Cyber-physical systems (CPS) and the Internet-of-Things (IoT) result in a tremendous amount of generated, measured and recorded time-series data. Extracting temporal segments that encode patterns with useful information out of these huge amounts of data is an extremely difficult problem. We propose shape expressions as a declarative formalism for specifying, querying and extracting sophisticated temporal patterns from possibly noisy data. Shape expressions are regular expressions with arbitrary (linear, exponential, sinusoidal, etc.) shapes with parameters as atomic predicates and additional constraints on these parameters. We equip shape expressions with a novel noisy semantics that combines regular expression matching semantics with statistical regression. We characterize essential properties of the formalism and propose an efficient approximate shape expression matching procedure. We demonstrate the wide applicability of this technique on two case studies.