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Time series analysis

A number of factors make it difficult to obtain accurate results from climate time series. Some of these factors are:
  • uneven time spacing
  • autocorrelation (persistence, serial dependence)
  • non-normal distributions
  • uncertain timescales
CRA has developed, adapted, tested and proven numerous statistical analysis techniques which enhance the accuracy and reliability of time series analysis data. The success of these techniques, as well as the dual-disciplinary (both climate and statistics) approach to data analysis, has earned CRA a position of respect in professional journals which are highly regarded by the climate change industry.

Analysis types

  • Regression: linear, nonlinear, nonparametric, lagged
  • Spectral analysis: Lomb–Scargle, WOSA, red-noise test, cross-spectra, multitaper
  • Extreme value time series: risk analysis
  • Correlation: Pearson's, Spearman's, nonlinear
  • Stochastic processes: long memory, nonlinear dynamics

Estimation methods

  • Maximum likelihood
  • Nonparametric kernel
  • Bootstrap simulations: moving block, parametric, stationary, ordinary, pairwise
  • Timescale construction and simulation

Robustness evaluation

This answers how strongly the result depends on made assumptions.
  • Sensitivity analyses
  • Monte Carlo tests