1. A method for detecting an event from time-series data sequences, the method comprising: receiving, by a processor, the time-series data sequences of variable lengths, generated by a plurality of sensors, wherein the time-series data sequences comprise timestamp information; sampling the time-series data sequences at a uniform sampling rate for generating sample points; generating, by the processor, a plurality of pairs of sample points using the sample points, wherein the plurality of pairs of sample points are generated by pairing each sample point with one another; computing, by the processor, a distance matrix (D) and an angle matrix (A) for one or more pairs of the sample points of the plurality of pairs of sample points; generating, by the processor, a two-dimensional (2D) shape histogram for the time-series data sequences using the distance matrix (D) and the angle matrix (A), wherein the 2D shape histogram have a constant dimension and the 2D shape histogram represents a global distribution of the plurality of pairs of sample points, wherein the global distribution of the plurality of pairs of sample points is obtained by computing a shape context of each sample point and wherein the shape context of each sample point is defined based on a distance-angle based distribution of each sample point with respect to remaining sample points; concatenating, by the processor, the 2D shape histograms for one or more time-series data sequences based on the timestamp information to generate a concatenated shape histogram; and matching, by the processor, the concatenated shape histogram with a pre-stored set of shape histograms to detect an event.