A similarity measure for temporal pattern discovery in time series data generated by IoT

Internet of Things implicitly generates myriads of temporal data. Unlocking such temporal data becomes a huge concern. Discovery and prediction of repeating temporal patterns and understanding the underlying temporal trends is much more challenging in the case of time stamped temporal data. At present, existing approaches do not reveal seasonal patterns, emerging or diminishing patterns. Determining similar temporal patterns and unearthing eccentric patterns require an efficient dissimilarity measure. This research addresses the similarity measure for revealing similar temporal patterns from time series data generated by IoT.