21 Exploring Lagged Effects in Time Series Andréa Puzzi Nicolau , Karen Dyson , David Saah , and Nicholas Clinton Overview In this chapter, we will introduce lagged effects to build on the previous work in mod- eling time series data. Time-lagged effects occur when an event at one point in time impacts dependent variables at a later point in time. You will be introduced to con- cepts of autocovariance and autocorrelation, cross-covariance and cross-correlation, and auto-regressive models. At the end of this chapter, you will be able to examine how variables relate to one another across time and to fit time series models that take into account lagged events. A. P. Nicolau · K. Dyson · D. Saah Spatial Informatics Group, Pleasanton, CA, USA e-mail: apnicolau@sig-gis.com K. Dyson e-mail: kdyson@sig-gis.com A. P. Nicolau · K. Dyson SERVIR-Amazonia, Cali, Colombia D. Saah (B) University of San Francisco, San Francisco, CA, USA e-mail: dssaah@usfca.edu N. Clinton Google LLC, Mountain View, CA, USA e-mail: nclinton@google.com K. Dyson Dendrolytics, Seattle, WA, USA © The Author(s) 2024 J. A. Cardille et al. (eds.), Cloud-Based Remote Sensing with Google Earth Engine, https://doi.org/10.1007/978-3-031-26588-4_21 403