References
Allaire J, Xie Y, Dervieux C, et al (2023) Rmarkdown: Dynamic documents
for r. R package version 2.22, https://CRAN.R-project.org/package=rmarkdown
Benjamin MA, Rigby RA, Stasinopoulos DM (2003) Generalized
autoregressive moving average models. Journal of the American
Statistical Association 98:214–223. https://doi.org/10.1198/016214503388619238
Benson B, Magnuson J, Sharma S (2020) Global lake and river ice
phenology database. Version 1 (G01377). National
Snow and Ice Data Center, Boulder, CO, USA
Berk RA (2016) Statistical learning
from a regression perspective, 2nd edn. Springer, Switzerland
Bickel PJ, Götze F, Zwet WR van (1997) Resampling fewer than n observations: Gains, losses, and
remedies for losses. Statistica Sinica 7:1–31
Bollerslev T (1986) Generalized autoregressive conditional
heteroskedasticity. Journal of Econometrics 31:307–327. https://doi.org/10.1016/0304-4076(86)90063-1
Bollerslev T (2009) Glossary to
ARCH (GARCH). In: Volatility and time
series econometrics: Essays in honour of Robert F. Engle.
SSRN
Borchers HW (2022) Pracma: Practical numerical math functions. R package
version 2.4.2, https://CRAN.R-project.org/package=pracma
Box GEP, Jenkins GM (1976) Time series analysis: Forecasting and
control. Holden-Day, San Francisco, CA, USA
Brockwell PJ, Davis RA (2002) Introduction to time series and
forecasting, 2nd edn. Springer, New York, NY, USA
Brooks C, Burke SP (2003) Information criteria for GARCH
model selection. The European Journal of Finance 9:557–580. https://doi.org/10.1080/1351847021000029188
Bühlmann P (2002) Bootstraps for time series. Statistical Science
17:52–72. https://doi.org/10.1214/ss/1023798998
Bunn A, Korpela M, Biondi F, et al (2022) dplR: Dendrochronology program
library in r. R package version 1.7.4, https://github.com/AndyBunn/dplR
Cabilio P, Zhang Y, Chen X (2013) Bootstrap rank tests for trend in time
series. Environmetrics 24:537–549. https://doi.org/10.1002/env.2250
Caeiro F, Mateus A (2022) Randtests: Testing randomness in r. R package
version 1.0.1, https://CRAN.R-project.org/package=randtests
Campbell SD, Diebold FX (2005) Weather forecasting for weather
derivatives. Journal of the American Statistical Association 100:6–16.
https://doi.org/10.1198/016214504000001051
Campos MC, Costa JL, Quintella BR, et al (2008) Activity and movement
patterns of the Lusitanian toadfish inferred from
pressure-sensitive data-loggers in the Mira estuary
(Portugal). Fisheries Management and Ecology 15:449–458. https://doi.org/10.1111/j.1365-2400.2008.00629.x
Chatfield C (2000) Time-series forecasting. CRC Press, Boca Raton, FL,
USA
Chatterjee S, Hadi AS (2006) Regression analysis by example, 4th edn.
John Wiley & Sons, Hoboken, NJ, USA
Chatterjee S, Simonoff JS (2013) Handbook of regression analysis. John
Wiley & Sons, Hoboken, NJ, USA
Cleveland WS (1979) Robust locally weighted regression and smoothing
scatterplots. Journal of the American Statistical Association
74:829–836. https://doi.org/10.1080/01621459.1979.10481038
Cochrane D, Orcutt GH (1949) Application of least squares regression to
relationships containing auto-correlated error terms. Journal of the
American Statistical Association 44:32–61. https://doi.org/10.2307/2280349
Cripps E, Dunsmuir WTM (2003) Modeling the variability of
Sydney Harbor wind measurements. Journal of
Applied Meteorology 42:1131–1138. https://doi.org/10.1175/1520-0450(2003)042<1131:MTVOSH>2.0.CO;2
Croissant Y, Graves S (2022) Ecdat: Data sets for econometrics. R
package version 0.4-2, https://www.r-project.org
Davison AC, Hinkley DV (1997) Bootstrap methods and their application.
Cambridge University Press, Cambridge, UK
Dean RT, Dunsmuir WTM (2016) Dangers and uses of cross-correlation in
analyzing time series in perception, performance, movement, and
neuroscience: The importance of constructing transfer function
autoregressive models. Behavior Research Methods 48:783–802. https://doi.org/10.3758/s13428-015-0611-2
Degras D, Xu Z, Zhang T, Wu WB (2012) Testing for parallelism among
trends in multiple time series. IEEE Transactions on Signal
Processing 60:1087–1097. https://doi.org/10.1109/TSP.2011.2177831
Dickey DA, Fuller WA (1979) Distribution of the estimators for
autoregressive time series with a unit root. Journal of the American
Statistical Association 74:427–431. https://doi.org/10.2307/2286348
Duguay CR, Brown L, Kang K-K, Kheyrollah Pour H (2013) State of the
climate in 2012: Lake ice. Bulletin of the American Meteorological
Society 94:S124–S126. https://doi.org/10.1175/2013BAMSStateoftheClimate.1
Dunn PK, Smyth GK (1996) Randomized quantile residuals. Journal of
Computational and Graphical Statistics 5:236–244. https://doi.org/10.2307/1390802
Efron B (1979) Bootstrap methods: Another look at the jackknife. The
Annals of Statistics 7:1–26. https://doi.org/10.1214/aos/1176344552
Engle RF (1982) Autoregressive conditional heteroscedasticity with
estimates of the variance of United Kingdom
inflation. Econometrica 50:987–1007. https://doi.org/10.2307/1912773
Engle RF, Granger CWJ (1987) Co-integration and error correction:
Representation, estimation, and testing. Econometrica 55:251–276. https://doi.org/10.2307/1913236
Esterby SR (1996) Review of methods for the detection and estimation of
trends with emphasis on water quality applications. Hydrological
Processes 10:127–149. https://doi.org/10.1002/(SICI)1099-1085(199602)10:2<127::AID-HYP354>3.0.CO;2-8
Eun CS, Lee J (2010) Mean-variance convergence around the world. Journal
of Banking & Finance 34:856–870. https://doi.org/10.1016/j.jbankfin.2009.09.016
Fasiolo M, Nedellec R (2021) mgcViz: Visualisations for generalized
additive models. R package version 0.1.9, https://github.com/mfasiolo/mgcViz
Gastwirth JL, Gel YR, Hui WLW, et al (2023) Lawstat: Tools for
biostatistics, public policy, and law. R package version 3.6, https://CRAN.R-project.org/package=lawstat
Granger CWJ (1969) Investigating causal relations by econometric models
and cross-spectral methods. Econometrica 37:424–438. https://doi.org/10.2307/1912791
Graves S (2019) FinTS: Companion to tsay (2005) analysis of financial
time series. R package version 0.4-6, https://r-forge.r-project.org/projects/fints/
Gupta PL, Gupta RC, Tripathi RC (1996) Analysis of zero-adjusted count
data. Computational Statistics & Data Analysis 23:207–218. https://doi.org/10.1016/S0167-9473(96)00032-1
Hall P, Van Keilegom I (2003) Using difference-based methods for
inference in nonparametric regression with time series errors. Journal
of the Royal Statistical Society: Series B (Statistical Methodology)
65:443–456. https://doi.org/10.1111/1467-9868.00395
Hansen PR, Lunde A (2005) A comparison of volatility models: Does
anything beat a GARCH(1, 1)? Journal of Applied
Econometrics 20:873–889. https://doi.org/10.1002/jae.800
Härdle W, Horowitz J, Kreiss J-P (2003) Bootstrap methods for time
series. International Statistical Review 71:435–459. https://doi.org/10.1111/j.1751-5823.2003.tb00485.x
Hastie TJ, Tibshirani RJ, Friedman JH (2009) The elements of
statistical learning: Data mining, inference, and prediction, 2nd
edn. Springer, New York, NY, USA
Hirsch RM, Slack JR, Smith RA (1982) Techniques of trend analysis for
monthly water quality data. Water Resources Research 18:107–121. https://doi.org/10.1029/WR018i001p00107
Hothorn T, Zeileis A, Farebrother RW, Cummins C (2022) Lmtest: Testing
linear regression models. R package version 0.9-40, https://CRAN.R-project.org/package=lmtest
Hyndman R (2023) Fma: Data sets from "forecasting: Methods and
applications" by makridakis, wheelwright & hyndman (1998). R package
version 2.5, https://CRAN.R-project.org/package=fma
Hyndman R, Athanasopoulos G, Bergmeir C, et al (2023) Forecast:
Forecasting functions for time series and linear models. R package
version 8.21, https://CRAN.R-project.org/package=forecast
Kassambara A (2023) Ggpubr: ggplot2 based publication ready plots. R
package version 0.6.0, https://rpkgs.datanovia.com/ggpubr/
Kelley D, Richards C (2023) Oce: Analysis of oceanographic data. R
package version 1.8-1, https://dankelley.github.io/oce/
Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin,
London, UK
Kirchgässner G, Wolters J (2007) Introduction to modern
time series analysis. Springer-Verlag, Berlin, Germany
Kohn R, Schimek MG, Smith M (2000) Spline and kernel
regression for dependent data. In: Schimek MG (ed) Smoothing and
regression: Approaches, computation, and application. John Wiley &
Sons, Inc., New York, pp 135–158
Kreiss J-P, Paparoditis E, Politis DN (2011) On the range of validity of
the autoregressive sieve bootstrap. Annals of Statistics 39:2103–2130.
https://doi.org/10.1214/11-AOS900
Krispin R (2020) TSstudio: Functions for time series analysis and
forecasting. R package version 0.1.6, https://github.com/RamiKrispin/TSstudio
Latifovic R, Pouliot D (2007) Analysis of climate change impacts on lake
ice phenology in Canada using the historical satellite data
record. Remote Sensing of Environment 106:492–507. https://doi.org/10.1016/j.rse.2006.09.015
Li WK (1994) Time series models based on generalized linear models: Some
further results. Biometrics 50:506–511. https://doi.org/10.2307/2533393
Ligges U, Short T, Kienzle P (2021) Signal: Signal processing. R package
version 0.7-7, https://CRAN.R-project.org/package=signal
Ljung GM, Box GEP (1978) On a measure of lack of fit in time series
models. Biometrika 65:297–303. https://doi.org/10.1093/biomet/65.2.297
Lomb NR (1976) Least-squares frequency analysis of unequally spaced
data. Astrophysics and Space Science 39:447–462. https://doi.org/10.1007/BF00648343
Lyubchich V (2016) Detecting time series trends and their
synchronization in climate data. Intelligence Innovations Investments
12:132–137. https://www.researchgate.net/publication/318283780_Detecting_time_series_trends_and_their_synchronization_in_climate_data
Lyubchich V, Gel YR (2016) A local factor nonparametric test for trend
synchronism in multiple time series. Journal of Multivariate Analysis
150:91–104. https://doi.org/10.1016/j.jmva.2016.05.004
Lyubchich V, Gel YR, El‐Shaarawi A (2013) On detecting non‐monotonic
trends in environmental time series: A fusion of local regression and
bootstrap. Environmetrics 24:209–226. https://doi.org/10.1002/env.2212
Lyubchich V, Gel YR, Vishwakarma S (2023) Funtimes: Functions for time
series analysis. R package version 9.1, https://CRAN.R-project.org/package=funtimes
Lyubchich V, Nesslage G (2020) Environmental drivers of golden tilefish fisheries
v1.0. Version v1.0. Zenodo
Lyubchich V, Wang X, Heyes A, Gel YR (2016) A distribution-free m-out-of-n bootstrap approach to testing
symmetry about an unknown median. Computational Statistics & Data
Analysis 104:1–9. https://doi.org/10.1016/j.csda.2016.05.004
Marinova D, McAleer M (2003) Modelling trends and volatility in
ecological patents in the USA. Environmental Modelling
& Software 18:195–203. https://doi.org/10.1016/S1364-8152(02)00079-8
McLeod AI (2022) Kendall: Kendall rank correlation and mann-kendall
trend test. R package version 2.2.1, http://www.stats.uwo.ca/faculty/aim
Nason GP (2008) Wavelet methods in
statistics with R. Springer, New York, NY, USA
Nesslage G, Lyubchich V, Nitschke P, et al (2021) Environmental drivers
of golden tilefish (Lopholatilus
chamaeleonticeps) commercial landings and catch-per-unit-effort.
Fisheries Oceanography 30:608–622. https://doi.org/10.1111/fog.12540
Noguchi K, Gel YR, Duguay CR (2011) Bootstrap-based tests for trends in
hydrological time series, with application to ice phenology data.
Journal of Hydrology 410:150–161. https://doi.org/10.1016/j.jhydrol.2011.09.008
O’Hara-Wild M, Hyndman R, Wang E (2023a) Fable: Forecasting models for
tidy time series. R package version 0.3.3, https://CRAN.R-project.org/package=fable
O’Hara-Wild M, Hyndman R, Wang E (2023b) Feasts: Feature extraction and
statistics for time series. R package version 0.3.1, https://CRAN.R-project.org/package=feasts
Park C, Hannig J, Kang K-H (2014) Nonparametric comparison of multiple
regression curves in scale-space. Journal of Computational and Graphical
Statistics 23:657–677. https://doi.org/10.1080/10618600.2013.822816
Park C, Vaughan A, Hannig J, Kang K-H (2009) SiZer analysis
for the comparison of time series. Journal of Statistical Planning and
Inference 139:3974–3988. https://doi.org/10.1016/j.jspi.2009.05.003
Pearl J (2009) Causality: Models, reasoning, and inference, 2nd edn.
Cambridge University Press, Cambridge, UK
Pedersen TL (2022) Patchwork: The composer of plots. R package version
1.1.2, https://CRAN.R-project.org/package=patchwork
Perperoglou A, Sauerbrei W, Abrahamowicz M, Schmid M (2019) A review of
spline function procedures in R. BMC Medical Research
Methodology 19: https://doi.org/10.1186/s12874-019-0666-3
Pfaff B (2022) Urca: Unit root and cointegration tests for time series
data. R package version 1.3-3, https://CRAN.R-project.org/package=urca
Pinheiro J, Bates D, R Core Team (2023) Nlme: Linear and nonlinear mixed
effects models. R package version 3.1-162, https://svn.r-project.org/R-packages/trunk/nlme/
Powell AM, Xu J (2011) Abrupt climate regime shifts, their potential
forcing and fisheries impacts. Atmospheric and Climate Sciences 1:33. https://doi.org/10.4236/acs.2011.12004
Rebane G, Pearl J (1987) The recovery of causal poly-trees from
statistical data. In: Proceedings of the third annual conference on
uncertainty in artificial intelligence. pp 222–228
Rice J (1984) Bandwidth choice for nonparametric regression. The Annals
of Statistics 12:1215–1230. https://doi.org/10.1214/aos/1176346788
Ruf T (1999) The Lomb–Scargle periodogram in biological
rhythm research: Analysis of incomplete and unequally spaced
time-series. Biological Rhythm Research 30:178–201. https://doi.org/10.1076/brhm.30.2.178.1422
Ruf T, C original by Press et al. partially based on, Python module
Astropy. the (2022) Lomb: Lomb-scargle periodogram. R package version
2.1.0, https://CRAN.R-project.org/package=lomb
Rydberg TH (2000) Realistic statistical modelling of financial data.
International Statistical Review 68:233–258. https://doi.org/10.2307/1403412
Scargle JD (1982) Studies in astronomical time series analysis.
II – statistical aspects of spectral analysis of unevenly
spaced data. Astrophysical Journal 263:835–853. https://doi.org/10.1086/160554
Schloerke B, Cook D, Larmarange J, et al (2021) GGally: Extension to
ggplot2. R package version 2.1.2, https://CRAN.R-project.org/package=GGally
Seidel DJ, Lanzante JR (2004) An assessment of three alternatives to
linear trends for characterizing global atmospheric temperature changes.
Journal of Geophysical Research: Atmospheres 109: https://doi.org/10.1029/2003JD004414
Shumway RH, Stoffer DS (2011) Time series analysis
and its applications with R examples, 3rd edn.
Springer, New York, NY, USA
Shumway RH, Stoffer DS (2014) Time series analysis and its applications
with R examples, 3-EZ. Free Texts in Statistics
Shumway RH, Stoffer DS (2017) Time series analysis
and its applications with R examples, 4th edn.
Springer, New York, NY, USA
Sievert C, Parmer C, Hocking T, et al (2023) Plotly: Create interactive
web graphics via plotly.js. R package version 4.10.2, https://CRAN.R-project.org/package=plotly
Siskey MR, Lyubchich V, Liang D, et al (2016) Periodicity of strontium:calcium across annuli further validates
otolith-ageing for Atlantic bluefin tuna
(Thunnus thynnus). Fisheries Research 177:13–17. https://doi.org/10.1016/j.fishres.2016.01.004
Soliman M, Lyubchich V, Gel YR (2019) Complementing the power of deep
learning with statistical model fusion: Probabilistic forecasting of
influenza in Dallas County, Texas, USA. Epidemics
28:100345. https://doi.org/10.1016/j.epidem.2019.05.004
Stasinopoulos DM, Rigby RA (2007) Generalized additive models for
location scale and shape (GAMLSS) in R.
Journal of Statistical Software 23:1–46. https://doi.org/10.18637/jss.v023.i07
Stasinopoulos M, Rigby B (2023) Gamlss: Generalised additive models for
location scale and shape. R package version 5.4-12, https://www.gamlss.com/
Stasinopoulos M, Rigby B, De Bastiani F, Merder J (2023) Gamlss.ggplots:
Plotting generalised additive model for location, scale and shape. R
package version 2.1-2, https://www.gamlss.com/
Stasinopoulos M, Rigby B, Eilers P (2016) Gamlss.util: GAMLSS utilities.
R package version 4.3-4, http://www.gamlss.org/
Stoffer D, Poison N (2023) Astsa: Applied statistical time series
analysis. R package version 2.0, https://CRAN.R-project.org/package=astsa
Taylor JW, Buizza R (2004) A comparison of temperature density forecasts
from GARCH and atmospheric models. Journal of Forecasting
23:337–355. https://doi.org/10.1002/for.917
Trapletti A, Hornik K (2023) Tseries: Time series analysis and
computational finance. R package version 0.10-54, https://CRAN.R-project.org/package=tseries
Tsay RS (2005) Analysis of financial time series, 2nd edn. John Wiley
& Sons, Hoboken, NJ, USA
Vilar-Fernández JM, González-Manteiga W (2004) Nonparametric comparison
of curves with dependent errors. Statistics 38:81–99. https://doi.org/10.1080/02331880310001634656
Vogelsang TJ, Franses PH (2005) Testing for common deterministic trend
slopes. Journal of Econometrics 126:1–24. https://doi.org/10.1016/j.jeconom.2004.02.004
Wang L, Akritas MG, Van Keilegom I (2008) An ANOVA-type
nonparametric diagnostic test for heteroscedastic regression models.
Journal of Nonparametric Statistics 20:365–382. https://doi.org/10.1080/10485250802066112
Wickham H (2023) Downlit: Syntax highlighting and automatic linking. R
package version 0.4.3, https://CRAN.R-project.org/package=downlit
Wickham H, Chang W, Henry L, et al (2023a) ggplot2: Create elegant data
visualisations using the grammar of graphics. R package version 3.4.2,
https://CRAN.R-project.org/package=ggplot2
Wickham H, François R, Henry L, et al (2023b) Dplyr: A grammar of data
manipulation. R package version 1.1.2, https://CRAN.R-project.org/package=dplyr
Wickham H, Hester J, Bryan J (2023c) Readr: Read rectangular text data.
R package version 2.1.4, https://CRAN.R-project.org/package=readr
Wickham H, Hester J, Ooms J (2023d) xml2: Parse XML. R package version
1.3.4, https://CRAN.R-project.org/package=xml2
Wood S (2023) Mgcv: Mixed GAM computation vehicle with automatic
smoothness estimation. R package version 1.8-42, https://CRAN.R-project.org/package=mgcv
Wood SN (2006) Generalized additive models: An introduction with r.
Chapman; Hall/CRC, New York, NY, USA
Wooldridge JM (2013) Introductory econometrics: A modern approach, 5th
edn. Cengage Learning, Mason, OH, USA
Wuertz D, Chalabi Y, Setz T, Maechler M (2022) fGarch: Rmetrics -
autoregressive conditional heteroskedastic modelling. R package version
4022.89, https://www.rmetrics.org
Xie Y (2023) Knitr: A general-purpose package for dynamic report
generation in r. R package version 1.42, https://yihui.org/knitr/
Zeger SL, Qaqish B (1988) Markov regression models for time series: A
quasi-likelihood approach. Biometrics 44:1019–1031. https://doi.org/10.2307/2531732
Zeileis A (2019) Dynlm: Dynamic linear regression. R package version
0.3-6, https://CRAN.R-project.org/package=dynlm
Zhang T (2013) Clustering high-dimensional time series based on
parallelism. Journal of the American Statistical Association
108:577–588. https://doi.org/10.1080/01621459.2012.760458
Zuur A, Ieno EN, Walker NJ, et al (2009) Mixed effects models
and extensions in ecology with R. Springer, New York