1. A computer-implemented method for analyzing time-series data based on world events derived from unstructured content obtained from a plurality of data sources, the method comprising: extracting, by a processor, from the unstructured content, a plurality of world events comprising at least one world event by: obtaining, by the processor, a meta-data associated with the unstructured content, wherein the meta-data is indicative of attributes of the unstructured content; de-duplicating, by the processor, the unstructured content based on the obtained meta-data associated with the unstructured content; and grouping, by the processor, similar unstructured content into a plurality of groups, wherein the unstructured content present in each of the plurality of groups corresponds to a world event; obtaining, by the processor, event information corresponding to the at least one world event from the unstructured content, wherein the event information includes at least time of occurrence of the at least one world event, time of termination of the at least one world event, at least one entity associated with the at least one world event, and a pre-assigned event class; retrieving, by the processor, time-series data pertaining to the at least one entity associated with the at least one world event, from a time-series data repository; and aligning, by the processor, the at least one world event with at least one time-series event based on the event information and the time-series data, to identify at least one pattern indicative of a cause-effect relationship between the at least one world event and the at least one time-series event.