Barnett, William A., David F. Hendry, Svend Hylleberg, Timo Teräsvirta, Dag Tjøstheim, and Allan Würtz (2006). Nonlinear Econometric Modeling in Time Series. Cambridge University Press.
Beran, Jan, Yuanhua Feng, Sucharita Ghosh, and Rafal Kulik (2013). Long-Memory Processes: Probabilistic Properties and Statistical Methods. Springer.
Bosq, Denis (1998). Nonparametric Statistics for Stochastic Processes: Estimation and Prediction. Springer.
Box, George E. P., Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung (2015). Time Series Analysis: Forecasting and Control, 5th ed.. Wiley
Brockwell, Peter J. and Richard A. Davis (1991). Time Series: Theory and Methods, 2nd. ed.. Springer. ITSM2000 Windows software is available here.
Brockwell, Peter J. and Richard A. Davis (2016). Introduction to Time Series and Forecasting, 3rd. ed.. Springer.
Casals, Jose, Alfredo Garcia-Hiernaux, Miguel Jerez, Sonia Sotoca, and A. Alexandre Trindade (2016). State-Space Methods for Time Series Analysis Theory, Applications and Software. CRC Press. E4 is the associated Matlab toolbox.
Chatfield, Chris, and Haipeng Xing (2019). The Analysis of Time Series: An Introduction with R. CRC Press. Code here.
Commandeur, Jacques J.F. and Siem Jan Koopman (2007). An Introduction to State Space Time Series Analysis. Oxford Univerity Press.
Cryer, Jonathan D., and Kung-Sik Chan (2008). Time Series Analysis: With Applications in R. Springer. TSA is the associated R package, and code and data are at Chan's site.
De Gooijer, Jan G. (2017). Elements of Nonlinear Time Series Analysis and Forecasting. Code and data here.
Deistler, Manfred, and Wolfgang Scherrer (2022). Time Series Models. Springer
Diebold, Francis (2024). Forecasting in Economics, Business, Finance and Beyond. Self-published, free. Course site here, data and code here.
Douc, Randal, Eric Moulines, and David Stoffer (2014). Nonlinear Time Series: Theory, Methods and Applications with R Examples. Chapman & Hall. nltsa is the R package associated with the book, and R code listings are here.
Durbin, James, and Siem Jan Koopman (2012). Time Series Analysis by State Space Methods, 2nd. ed.. Oxford University Press. A paper co-authored by Koopman is Statistical Software for State Space Methods.
Fan, Jianqing and Qiwei Yao (2003). Nonlinear Time Series: Nonparametric and Parametric Methods. Springer. Codes at Fan's site.
Francq, Christian, and Jean-Michel Zakoian (2019). GARCH Models: Structure, Statistical Inference and Financial Applications. Wiley. R and Fortran codes are at Francq's site.
Franses, Philip Hans, and Dick van Dijk (2000). Non-Linear Time Series Models in Empirical Finance. Cambridge University Press
Frühwirth-Schnatter, Sylvia (2006). Finite Mixture and Markov Switching Models. Springer. Bayesf is a Matlab package associated with the book.
Gómez, Victor (2019). Linear Time Series with MATLAB and OCTAVE. Springer. SSMMATLAB is the associated Matlab package.
Gouriéroux, Christian (1997). ARCH Models and Financial Applications. Springer.
Granger, C. W. J., and Michio Hatanaka (1964). Spectral Analysis of Economic Time Series. Princeton University Press
Engle, Robert F., and C. W. J. Granger (1992). Long-Run Economic Relationships: Readings in Cointegration. Oxford University Press.
Hagiwara, Junichiro (2021). Time Series Analysis for the State-Space Model with R/Stan. Springer. Author's repo.
Hamilton, James D. (1994). Time Series Analysis. Princeton University Press. Code at Hamilton's site
Hannan, E. J., and Manfred Deistler (1988). The Statistical Theory of Linear Systems. SIAM.
Harvey, Andrew C. (1990). The Econometric Analysis of Time Series. MIT Press.
Harvey, Andrew C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Harvey, Andrew C. (1993). Time Series Models, 2nd ed.. MIT Press.
Harvey, Andrew C., Siem Jan Koopman, and Neil Shephard, editors (2004). State Space and Unobserved Component Models: Theory and Applications. Cambridge University Press.
Harvey, Andrew C. (2013). Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press. Time Series Lab is associated software, and GAS is a related R package.
Hassler, Uwe (2018). Time Series Analysis with Long Memory in View. Wiley.
Huang, Changquan, and Alla Petukhina (2022). Applied Time Series Analysis and Forecasting with Python. PythonTsa is the associated Python package, with other code at Petukhina's repo.
Hyndman, Rob J., Anne B. Koehler, J. Keith Ord, and Ralph D. Snyder (2008). Forecasting with Exponential Smoothing: the State Space Approach. Springer. Associated R packages are forecast, fable, and expsmooth, with R code and data at Hyndman's site
Hyndman, Rob J., and George Athanasopoulos (2021). Forecasting: Principles and Practice, 3rd ed.. OTexts. Tsibble and fable are associated R packages, and the text of the book is available online.
Johansen, Søren (1995). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press.
Kantz, Holger, and Thomas Schreiber (2010), Nonlinear Time Series Analysis, 2nd. ed.. Cambridge University Press. TISEAN is associated software, with tseriesChaos a related R package.
Kim, Chang-Jin, and Charles R. Nelson (1999). State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. MIT Press.
Kitagawa, Genshiro, and Will Gersch (1996). Smoothness Priors Analysis of Time Series. Springer
Kitagawa, Genshiro (2020). Introduction to Time Series Modeling with Applications in R. Routledge. A related R package is TSSS (Time Series Analysis with State Space Model)
Li, Ta-Hsin (2014). Time Series with Mixed Spectra. CRC Press. Qfa is the associated R package.
Lütkepohl, Helmut (2005). New Introduction to Multiple Time Series Analysis. Springer
Maddala, G. S., and In-Moo Kim (2010). Unit Roots, Cointegration, and Structural Change. Cambridge University Press
Mills, Terence C., and Raphael N. Markellos (2008). The Econometric Modelling of Financial Time Series, 3rd ed., Cambridge University Press.
Palma, Wilfredo (2007). Long-Memory Time Series: Theory and Methods. Some data and R and S+ code is at the author's site.
Peña, Daniel, and Ruey S. Tsay (2021). Statistical Learning for Big Dependent Data. Wiley. SLBDD is the associated R package.
Petris, Giovanni, Sonia Petrone, and Patrizia Campagnoli (2009). Dynamic Linear Models with R. Springer. Dlm is the associated R package.
Pfaff, Bernhard (2008). Analysis of Integrated and Cointegrated Time Series with R, 2nd ed.. Springer. Urca is an associated R package.
Pole, Andy, Mike West, Jeff Harrison (1994). Applied Bayesian Forecasting and Time Series Analysis. BATS software is at West's site, as is other software.
Prado, Raquel, Marco A. R. Ferreira, and Mike West (2021). Time Series: Modeling, Computation, and Inference. CRC Press. Code at West's site.
Rao, B. Bhaskara (1994). Cointegration for the Applied Economist. Springer
Reinsel, Gregory C. (1997). Elements of Multivariate Time Series Analysis, 2nd ed.. Springer
Robinson, Peter M. (2003). Time Series With Long Memory. Oxford University Press
Shephard, Neil, editor (2005). Stochastic Volatility: Selected Readings. Oxford University Press.
Shumway, Robert H., and David S. Stoffer (2025). Time Series Analysis and Its Applications: With R Examples. Springer. ASTSA is the associated R package, and other code is here.
Svetunkov, Ivan (2024). Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM). CRC. Greybox and smooth are associated R packages, and the text of the book is available here.
Takahashi, Makoto, Yasuhiro Omori, Toshiaki Watanabe (2023). Stochastic Volatility and Realized Stochastic Volatility Models. Springer. ASV: Stochastic Volatility Models with or without Leverage is an R package co-authored by Omori, and other software is at Omori's site.
Taylor, Stephen J. (2007). Modelling Financial Time Series, 2nd ed.. World Scientific.
Taylor, Stephen J. (2011). Asset Price Dynamics, Volatility, and Prediction. Princeton Univerity Press.
Teräsvirta, Timo, Dag Tjøstheim, and Clive W. J. Granger (2010). Modelling Nonlinear Economic Time Series. Oxford University Press.
Tong, Howell (1993). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.
Triantafyllopoulos, Kostas (2021). Bayesian Inference of State Space Models: Kalman Filtering and Beyond. Springer.
Tsay, Ruey S. (2010). Analysis of Financial Time Series, 3rd ed.. Wiley. Data and codes are at the author's site
Tsay, Ruey S. (2014). Multivariate Time Series Analysis: with R and Financial Applications. Wiley. MTS is the associated R package, and other R codes are at the author's site.
Tsay, Ruey S., and Rong Chen (2018). Nonlinear Time Series Analysis. Wiley. NTS is the associated R package.
Visser, Ingmar, and Maarten Speekenbrink (2022). Mixture and Hidden Markov Models with R. Hmmr and depmixS4.
Wei, William W. S. (2018). Multivariate Time Series Analysis and Applications. Wiley
Wei, William W. S. (2019). Time Series Analysis: Univariate and Multivariate Methods, 2nd ed.. Pearson
Weigend, Andreas S. (1994). Time Series Prediction: Forecasting The Future And Understanding The Past. Routledge.
West, Mike, and Jeff Harrison (1997). Bayesian Forecasting and Dynamic Models, 2nd. ed.. Springer
Woodward, Wayne A., Henry L. Gray, and Alan C. Elliott (2021). Applied Time Series Analysis with R, 2nd ed.. CRC Press. Tswge is an associated R package.
Zeng, Yong, and Shu Wu (2013). State-Space Models: Applications in Economics and Finance. Springer. Part I includes three chapters on Particle Filtering and Parameter Learning in Nonlinear State-Space Models. Part II includes four chapters on the application of Linear State-Space Models in Macroeconomics and Finance. Part III includes five chapters on Hidden Markov Models (HMM), Regime Switching, and Mathematical Finance.
Zucchini, Walter, Iain L. MacDonald, and Roland Langrock (2016). Hidden Markov Models for Time Series: An Introduction Using R, 2nd ed.. CRC Press. R codes are here, and R packages used are depmixS4, HiddenMarkov, and msm.