Mendis, R. A. C. Laksheen, authorPallickara, Sangmi Lee, advisorPallickara, Shrideep, committee memberHayne, Stephen, committee member2022-05-302022-05-302022https://hdl.handle.net/10217/235176Voluminous time-series observational data impose challenges pertaining to storage and analytics. Identifying patterns in such climate time-series data is critical for many geospatial applications. Over the recent years, clustering has become a key computational technique for identifying patterns/clusters. However, data with complex structures and high dimensions could lead to uninformative clusters and hinder the quality of clustering. In this research, we use the state-of-the-art autoencoders with LSTMs, Bidirectional LSTMs and GRUs to learn highly non-linear mapping functions by training the networks with subsequences of timeseries to perform data reconstruction. Next, we extract the trained encoders to generate embeddings which are lightweight. These embeddings are more space efficient than the original time series data and require less computational power and resources for further processing. In the final step of clustering, instead of using common distance-based metrics like Euclidean distance, we use DTW, an algorithm for computing similarity between time series by ignoring variations in speed, to calculate similarity between the embeddings during the application of k- Means algorithm. Based on Silhouette score, this method generates clusters which are better than other reduction techniques.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text