In the contemporary trading environment any competitive advantage is of core interest to technical traders and financial analysts. Motifs (i.e. time series patterns corresponding to repetitive behaviours) can be indicative of periods of auto regression and are often used for the identification of common trading patterns in financial series, broadly spanning market sectors and asset classes. Widely believed to have predictive value, the identification of these patterns may trigger a collective response by traders leading to overall market movements as a result. The task of motif discovery has attracted considerable attention in the literature, leading to significant recent improvements in terms of efficiency and scalability. However, to date these motif discovery algorithms, even those of variable length, identify sub-sequences of equal match side-length. Here we propose the concept of a side-length independent motif (SLIM) approach, where identification of similar behaviour is still achieved while permitting a length variability between each side of a motif match pair. SLIM facilitates the identification by traders of common financial patterns (such as Head & Shoulders for example), occurring over differing time horizons, thus extending pattern recognition possibilities and investment opportunities. Volatility patterns in financial series are also a primary focus, not only to traders but also more broadly to policy makers and economists, as it is considered analogous to market risk. Through analysis of compression rates, achieved by application of the minimum description length (MDL) principle to SAX representations of financial series, SLIM can also provide further insight into such volatility in the financial sector. Expressed as a percentage, SLIM allows the volatility of series across asset classes and differing time horizons to be visually and directly compared. In this thesis, a new algorithm entitled SLIM is introduced, which extends existing leading algorithms in the literature. The side-length independence concept of SLIM is outlined with its strengths and weaknesses discussed. A set of case studies is also presented throughout, highlighting specific financial applications and detailing the advantageous properties of SLIM for pattern recognition and volatility analysis. Results are discussed in detail while improvements and potential future directions of SLIM are also outlined.