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Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
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Time series smoother for effect detection.

Cheng You1, Dennis K J Lin1, S Stanley Young2

  • 1Department of Statistics, Pennsylvania State University, State College, PA, United States of America.

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|April 24, 2018
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Summary
This summary is machine-generated.

Environmental epidemiology often struggles with long-term trends obscuring short-term signals in time series data. This study introduces new time series smoothers to effectively detrend data and recover these crucial short-term signals.

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Area of Science:

  • Environmental Epidemiology
  • Time Series Analysis
  • Statistical Modeling

Background:

  • Environmental epidemiology frequently encounters time series data with complex long-term trends and seasonality.
  • Observed covariates often fail to fully adjust for these trends, making it difficult to isolate short-term signals of interest.
  • Existing spline smoothing methods can yield inconsistent results based on parameter selection, complicating trend separation.

Purpose of the Study:

  • To develop methods for estimating long-term trends in environmental time series data.
  • To recover subtle short-term signals that are obscured by underlying trends and seasonality.
  • To propose robust time series smoothers that minimize parameter tuning and improve signal recovery.

Main Methods:

  • Development and application of three novel classes of time series smoothers designed for detrending.
  • Evaluation of smoothers using a case study with a factorial design.
  • Validation through a simulation study employing datasets generated from the original data.

Main Results:

  • Demonstration that current spline smoothing can produce variable cross-correlations depending on parameter choices.
  • Proposed smoothers effectively detrend time series data without requiring extensive parameter fine-tuning.
  • Successful recovery of short-term signals from time series exhibiting long-term trends and seasonality.

Conclusions:

  • Identified a significant challenge in separating long-term trends from short-term signals in environmental epidemiology.
  • Characterized the properties of proposed time series smoothers as effective solutions for detrending.
  • Provided general guidelines for discovering short-term signals in complex time series data.