STR: Seasonal-Trend Decomposition Using Regression

References

• Allaire J, Xie Y, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2020) rmarkdown: Dynamic documents for R. R package version 2.5.3. https://github.com/rstudio/rmarkdown.Google Scholar
• Anderson VO, Nochmals U (1914) The elimination of spurious correlation due to position in time or space. Biometrika 10(2/3):269–279.Google Scholar
• Bell WR, Martin DEK (2004) Modeling time-varying trading-day effects in monthly time series. Proc. Joint Statist. Meetings, 8–12.Google Scholar
• Box GEP, Cox DR (1964) An analysis of transformations. J. Roy. Statist. Soc. Ser. B. Statist. Methodology 26(2):211–252.Google Scholar
• Box GEP, Tiao GC (1975) Intervention analysis with applications to economic and environmental problems. J. Amer. Statist. Assoc. 70(349):70–79.Google Scholar
• Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: A seasonal-trend decomposition procedure based on Loess. J. Official Statist. 6(1):3–73.Google Scholar
• Commandeur JJF, Koopman SJ, Ooms M (2011) Statistical software for state space methods. J. Statist. Software 41(1):1–18.Google Scholar
• Copeland MT (1915) Statistical indices of business conditions. Quart. J. Econom. 29(3):522–562.Google Scholar
• Dagum EB (1988) The X11ARIMA/88 Seasonal Adjustment Method: Foundations and User’s Manual (Statistics Canada, Time Series Research and Analysis Division).Google Scholar
• Dagum EB, Bianconcini S (2016) Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation (Springer, Switzerland).Google Scholar
• De Livera AM, Hyndman RJ, Snyder RD (2011) Forecasting time series with complex seasonal patterns using exponential smoothing. J. Amer. Statist. Assoc. 106(496):1513–1527.Google Scholar
• Dokumentov A, Hyndman RJ (2018) stR: STR decomposition. R package version 0.4. https://CRAN.R-project.org/package=stR.Google Scholar
• Findley DF (2005) Some recent developments and directions in seasonal adjustment. J. Official Statist. 21(2):343–365.Google Scholar
• Findley DF, Monsell BC, Hou CT (2012) Stock series holiday regressors generated by flow series holiday regressors. Taiwan Econom. Forecast Policy 43(1):75–122.Google Scholar
• Findley DF, Monsell BC, Bell WR, Otto MC, Chen BC (1998) New capabilities and methods of the X-12-ARIMA seasonal-adjustment program. J. Bus. Econom. Statist. 16(2):127–152.Google Scholar
• Gómez V, Maravall A (2001) Seasonal adjustment and signal extraction in economic time series. Peña D, Tiao GC, Tsay RS, eds. A Course in Time Series Analysis (John Wiley & Sons, New York), 202–246.Google Scholar
• Harvey AC (1990) Forecasting, Structural Time Series Models and the Kalman Filter (Cambridge University Press, Cambridge, UK).Google Scholar
• Hooker RH (1901) The suspension of the Berlin produce exchange and its effect upon corn prices. J. Roy. Statist. Soc. 64(4):574–613.Google Scholar
• Hyndman RJ, Wang E, Laptev N (2015) Large-scale unusual time series detection. Proc. IEEE Internat. Conf. Data Mining, 1616–1619.Google Scholar
• Hyndman RJ, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, et al.. (2021) forecast: Forecasting functions for time series and linear models. Version 8.14. https://CRAN.R-project.org/package=forecast.Google Scholar
• Koenker R (2005) Quantile Regression (Cambridge University Press, Cambridge, UK).Google Scholar
• Koenker R (2020) quantreg: Quantile regression. R package version 5.75. https://CRAN.R-project.org/package=quantreg.Google Scholar
• Ladiray D, Quenneville B (2001) Seasonal Adjustment with the X-11 Method. Lecture Notes in Statistics, vol. 158 (Springer, New York).Google Scholar
• Lubba CH, Sethi SS, Knaute P, Schultz SR, Fulcher BD, Jones NS (2019) catch22: Canonical time-series characteristics. Data Mining Knowledge Discovery 33(6):1821–1852.Google Scholar
• Macaulay FR (1931) The Smoothing of Time Series (NBER Books, New York).Google Scholar
• Montero-Manso P, Athanasopoulos G, Hyndman RJ, Talagala TS (2020) FFORMA: Feature-based forecast model averaging. Internat. J. Forecasting 36(1):86–92.Google Scholar
• Poynting JH (1884) A comparison of the fluctuations in the price of wheat and in the cotton and silk imports into Great Britain. J. Statist. Soc. London 47(1):34–74.Google Scholar
• R Core Team (2020) R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna).Google Scholar
• Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric Regression (Cambridge University Press, New York).Google Scholar
• Seber GAF, Lee AJ (2003) Linear Regression Analysis, 2nd ed. (John Wiley & Sons, New York).Google Scholar
• Shiskin J (1957) Electronic computers and business indicators. Occasional Paper 57, National Bureau of Economic Research, Washington, DC.Google Scholar
• Shiskin J, Young AH, Musgrave JC (1967) The X-11 variant of the Census II method seasonal adjustment program. Technical Report 15, Bureau of the Census, U.S. Department of Commerce, Washington, DC.Google Scholar
• Spencer J (1904) On the graduation of the rates of sickness and mortality presented by the experience of the Manchester Unity of Oddfellows during the period 1893–97. J. Inst. Actuaries 38(4):334–343.Google Scholar
• Talagala TS, Hyndman RJ, Athanasopoulos G (2018) Meta-learning how to forecast time series. Working Paper 6/18, Department of Econometrics & Business Statistics, Monash University, Clayton, Victoria, Australia.Google Scholar
• Wood SN (2006) Generalized Additive Models: An Introduction with R (CRC Press, Boca Raton, FL).Google Scholar