@article{khider_pyleoclim_2022,
	title = {Pyleoclim: {Paleoclimate} {Timeseries} {Analysis} and {Visualization} {With} {Python}},
	volume = {37},
	issn = {2572-4517, 2572-4525},
	shorttitle = {Pyleoclim},
	url = {https://onlinelibrary.wiley.com/doi/10.1029/2022PA004509},
	doi = {10.1029/2022PA004509},
	language = {en},
	number = {10},
	urldate = {2022-11-02},
	journal = {Paleoceanography and Paleoclimatology},
	author = {Khider, Deborah and Emile‐Geay, Julien and Zhu, Feng and James, Alexander and Landers, Jordan and Ratnakar, Varun and Gil, Yolanda},
	month = oct,
	year = {2022},
}

@inproceedings{khider_towards_2020,
	address = {San Diego, California, USA},
	title = {Towards automating time series analysis for the paleogeosciences},
	url = {https://github.com/khider/khider.github.io/blob/master/papers/KDD_TimeSeries_Workshop_revised.pdf},
	abstract = {There is an abundance of time series data in many domains. Analyz- ing this data effectively requires deep expertise acquired over many years of practice. Our goal is to develop automated systems for time series analysis that can take advantage of proven methods that yield the best results. Our work is motivated by paleogeosciences time series analysis where the datasets are very challenging and require sophisticated methods to find and quantify subtle patterns. We describe our initial implementation of AutoTS, an automated system for time series analysis that uses semantic workflows to rep- resent sophisticated methods and their constraints. AutoTS extends the WINGS workflow system with new capabilities to customize general methods to specific datasets based on key characteristics of the data. We discuss general methods for spectral analysis and their implementation in AutoTS.},
	publisher = {ACM, New York, NY, USA},
	author = {Khider, Deborah and Athreya, Pratheek and Ratnakar, Varun and Gil, Yolanda and Zhu, Feng and Kwan, Myron and Emile-Geay, Julien},
	year = {2020},
}

There is an abundance of time series data in many domains. Analyz- ing this data effectively requires deep expertise acquired over many years of practice. Our goal is to develop automated systems for time series analysis that can take advantage of proven methods that yield the best results. Our work is motivated by paleogeosciences time series analysis where the datasets are very challenging and require sophisticated methods to find and quantify subtle patterns. We describe our initial implementation of AutoTS, an automated system for time series analysis that uses semantic workflows to rep- resent sophisticated methods and their constraints. AutoTS extends the WINGS workflow system with new capabilities to customize general methods to specific datasets based on key characteristics of the data. We discuss general methods for spectral analysis and their implementation in AutoTS.