- Y. Hirata, K. Morino, K. Akakura, C. S. Higano, and K. Aihara, ``Personalizing androgen suppression for prostate cancer using mathematical modeling,'' Scientific Reports 8, 2673 (2018).
- Y. Hirata and K. Aihara, ``Ability of intermittent androgen suppression to seletively create a non-trivial periodic orbit for a type of prostate cancer patients,'' Journal of Theoretical Biology 384, 147-152 (2015).
- Y. Hirata, K. Morino, K. Akakura, C. S. Higano, N. Bruchovsky, T. Gambol, S. Hall, G. Tanaka, and K. Aihara, ``Intermittent androgen suppression: estimating parameters for individual patients based on initial PSA data in response to androgen deprivation therapy,'' PLoS One 10, e0130372 (2015).
- K. Morino, Y. Hirata, R. Tomioka, H. Kashima, K. Yamanishi, N. Hayashi, S. Egawa, and K. Aihara, ``Predicting disease progression from short biomarker series using expert advice algorithm,'' Sci. Rep. 5, 8953 (2015).
- T. Hatano, Y. Hirata, H. Suzuki, and K. Aihara, ``Comparison between mathematical models of intermittent androgen suppression for prostate cancer,'' J. Theor. Biol. 366, 33-45 (2015).
- Y. Hirata, S. Azuma, and K. Aihara, ``Model predictive control for optimally scheduling intermittent androgen suppression of prostate cancer,'' Methods 67, 278-281 (2014).
- Y. Suzuki, D. Sakai, T. Nomura, Y. Hirata, and K. Aihara, ``A new protocol for intermittent androgen suppression therapy for prostate cancer with unstable saddle-point dynamics,'' J. Theor. Biol. 350, 1-16 (2014).
- Q. Guo, Z. Lu, Y. Hirata, and K. Aihara, ``Parameter estimation and optimal scheduling algorithm for a mathematical model of intermittent androgen suppression therapy for prostate cancer,'' Chaos 23, 043125 (2013).
- H. Kuramae, Y. Hirata, N. Bruchovsky, K. Aihara, and H. Suzuki, ``Nonlinear systems identification by combining regression with bootstrap resampling,'' Chaos 21, 043121 (2011).
- Y. Hirata, M. di Bernardo, N. Bruchovsky, and K. Aihara, ``Hybrid optimal scheduling for intermittent androgen suppression of prostate cancer,'' Chaos 20, 045125 (2010).
- G. Tanaka, Y. Hirata, S. L. Goldenberg, N. Bruchovsky, and K. Aihara, ``Mathematical modelling of prostate cancer growth and its application to hormone therapy,'' Philos. T. R. Soc. Lond. A 368, 5029-5044 (2010).(Program for analyzing prostate cancer)
- Y. Hirata, N. Bruchovsky, and K. Aihara, ``Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer,'' Journal of Theoretical Biology 264, 517-527 (2010).(Program for analyzing prostate cancer)

- Y. Hirata, ``Reconstructing latent dynamical noise for better predicting observables,'' Chaos 28, 033112 (2018) (support page).
- J. Amigo, Y. Hirata, and K. Aihara, ``On the limits of probabilistic forecasting in nonlinear time series analysis II: Differential entropy,'' Chaos 27, 083125 (2017).
- Y. Hirata and K. Aihara, ``Improving time series prediction of solar irradiance after sunrise: Comparison among three methods for time series prediction,'' Solar Energy 149, 294-301 (2017).
- J. M. Amigo, Y. Hirata, and K. Aihara, ``On the limits of probabilistic forecasting in nonlinear time series analysis,'' Chaos 26, 123114 (2016).
- M. Chayama and Y. Hirata, ``When univariate model-free time series prediction is better than multivairate'' Phys. Lett. A 380, 2359-2365 (2016).
- Y. Hirata and K. Aihara, ``Predicting ramps by integrating different sorts of information,'' European Physical Journal Special Topics 225, 513-525 (2016).
- Y. Hirata, T. Takeuchi, S. Horai, H. Suzuki, and K. Aihara, ``Parsimonious description for predicting high-dimensional dynamics,'' Sci. Rep. 5, 15736 (2015).
- Y. Hirata, M. Shiro, N. Takahashi, K. Aihara, H. Suzuki, and P. Mas, ``Approximating high-dimensional dynamics by barycentric coordinates with linear programming,'' Chaos 25, 013114 (2015).
- S. Oya, K. Aihara, and Y. Hirata, ``Forecasting abrupt changes in foreign exchange markets: method using dynamical network marker,'' New J. Phys. 16, 115015 (2014).
- Y. Hirata, K. Aihara, and H. Suzuki, ``Predicting multivariate time series in real time with confidence intervals: applications to renewable energy,'' Eur. Phys. J. Spec. Top. 223, 2451-2460 (2014).
- Y. Hirata, ``Fast time-series prediction using high-dimensional data: Evaluating confidence interval credibility,'' Phys. Rev. E 89, 052916 (2014).
- Y. Hirata, T. Yamada, J. Takahashi, K. Aihara, and H. Suzuki, ``Online multi-step prediction for wind speeds and solar irradiation: evaluation of prediction errors,'' Renew. Energy 67, 35-39 (2014).
- E. P. Bravo, K. Aihara, and Y. Hirata, ``Application of joint permutations fro predicting coupled time series,'' Chaos 23, 043104 (2013).
- Y. Hirata, T. Yamada, J. Takahashi, and H. Suzuki, ``Real-time multi-step predictors from data streams,'' Phys. Lett. A 376, 3092-3097 (2012).
- Y. Hirata and K. Aihara, ``Describing high-dimensional dynamics with low-dimensional piecewise affine models: Applications to renewable energy,'' Chaos 22, 023143 (2012).
- Y. Hirata, D. P. Mandic, H. Suzuki, and K. Aihara, ``Wind direction modelling using multiple observation points,'' Philosophical Transactions of the Royal Society A 366, 591-607 (2008).
- Y. Hirata, H. Suzuki, and K. Aihara, ``Reconstructing state spaces from multivariate data using variable delays,'' Physical Review E 74, 026202 (2006).

- Y. Hirata, T. Stemler, D. Eroglu, and N. Marwan, ``Prediction of flowdynamics using point processes,'' Chaos 28, 011101 (2018).
- Y. Hirata and K. Aihara, ``Dimensionless embedding for nonlinear time series analysis,'' Phys. Rev. E 96, 032291 (2017).
- K. Iwayama, Y. Hirata, and K. Aihara, ``Definition of distance for nonlinear time series analysis of marked point process data,'' Phys. Lett. A 381, 257-262 (2017).
- Y. Hirata, K. Iwayama, and K. Aihara, ``Possibility of short-term probabilistic forecasts for large earthquakes making good use of the limitations of existing catalogs,'' Phys. Rev. E 94, 042217 (2016).
- Y. Shimada, Y. Hirata, T. Ikeguchi, and K. Aihara, ``Graph distance for complex networks,'' Sci. Rep. 6, 34944 (2016).
- Y. Hirata, A. Oda, K. Ohta, and K. Aihara, ``Three-dimensional reconstruction of single-cell chromosome structure using recurrence plots,'' Sci. Rep. 6, 34982 (2016).
- M. Fukino, Y. Hirata, and K. Aihara, ``Coarse-graining time series data: recurrence plot of recurrence plots and its application for music,'' Chaos 26, 023116 (2016). (Physics Today, SIAM News)
- Y. Hirata and K. Aihara, ``Edit distance for marked point processes revisited: an implementation by binary integer programming,'' Chaos 25, 123117 (2015).
- Y. Hirata, M. Komuro, S. Horai, and K. Aihara, ``Faithfulness of recurrence plots: a mathematical proof,'' Int. J. Bifurcat. Chaos 25, 1550168 (2015).
- S. Nakano, Y. Hirata, K. Iwayama, and K. Aihara, ``Intra-day response of foregin exchange markets after the Tohoku-Oki earthquake,'' Physica A 419, 203-214 (2015).
- S. Oya, K. Aihara, and Y. Hirata, ``An absolute measure for a key currency,'' Physica A 407, 15-23 (2014).
- K. Iwayama, Y. Hirata, H. Suzuki, and K. Aihara, ``Change-point detection with recurrence networks'', NOLTA J. 4, 160-171 (2013).
- K. Iwayama, Y. Hirata, K. Takahashi, K. Watanabe, K. Aihara, and H. Suzuki, ``Characterizing global evolutions of complex systems via intermediate network representations,'' Sci. Rep. 2, 423 (2012).
- Y. Hirata and K. Aihara, ``Timing matters in foreign exchange markets,'' Physica A 391, 760-766 (2012).
- Y. Hirata and K. Aihara, ``Statistical tests for serial dependence and laminarity on recurrence plots,'' Int. J. Bifurcat. Chaos 21, 1077-1084 (2011).
- Y. Hirata, Y. Shimo, H. L. Tanaka, and K. Aihara, ``Chaotic properties of the Arctic Oscillation Index,'' SOLA 7, 033-036 (2011).
- S. Suzuki, Y. Hirata, and K. Aihara, ``Definition of distance for marked point process data and its application to recurrence plot-based analysis of exchange tick data of foreign currencies,'' International Journal of Bifurcation and Chaos 20, 3699-3708 (2010).
- Y. Hirata and K. Aihara, ``Devaney's chaos on recurrence plots,'' Physical Review E 82, 036209 (2010).
- Y. Hirata and K. Aihara, ``Identifying hidden common causes from bivariate time series: a method using recurrence plots,'' Physical Review E 81, 016203 (2010).
- Y. Hirata and K. Aihara, ``Representing spike trains using constant sampling intervals,'' Journal of Neuroscience Methods 183, 277-286 (2009).
- M. Tanio, Y. Hirata, and H. Suzuki, ``Reconstruction of driving forces through recurrence plots,'' Physics Letters A 373, 2031-2040 (2009).
- Y. Hirata, S. Horai, and K. Aihara, ``Reproduction of distance matrices and original time series from recurrence plots and its applications,'' European Physical Journal Special Topics 164, 13-22 (2008).

- Y. Hirata and K. Aihara, ``Estimating optimal partitions for stochastic complex systems,'' Eur. Phys. J. Spec. Top. 222, 303-315 (2013).
- Y. Hirata, K. Judd, and K. Aihara, ``Characterizing chaotic response of a squid axon through generating partitions,'' Physics Letters A 346, 141-147 (2005).
- Y. Hirata and K. Judd, ``Constructing dynamical systems with specified symbolic dynamics,'' Chaos 15, 033102 (2005).
- Y. Hirata, K. Judd, and D. Kilminster, ``Estimating a generating partition from observed time series: Symbolic shadowing,'' Physical Review E 70, 016215 (2004).
- Y. Hirata and A. I. Mees, ``Estimating topological entropy via a symbolic data compression technique,'' Physical Review E 67, 026205 (2003).
- Y. Hirata, H. Shimokawa, and K. Aihara, ``Entropy function and source coding of 1-dimensional maps,'' IEICE Transactions J82-A, 1780-1792 (1999) (in Japanese).

- M. Shiro, Y. Hirata, and K. Aihara, ``Failure of pseudo-periodic surrogates,'' Artif. Life Robotics 15, 496-499 (2010).
- Y. Hirata, Y. Katori, H. Shimokawa, H. Suzuki, T. A. Blenkinsop, E. J. Lang, and K. Aihara, ``Testing a neural coding hypothesis using surrogate data,'' Journal of Neuroscience Methods 172, 312-322 (2008).
- Y. Hirata, S. Horai, H. Suzuki, and K. Aihara, ``Testing serial dependence by Random-shuffle surrogates and the Wayland method,'' Phys. Lett. A 370, 265-274 (2007).
- T. Nakamura, Y. Hirata, and M. Small, ``Testing for correlation structures in short term variabilities with long term trends of multivariate time series,'' Physical Review E 74, 041114 (2006).
- T. Nakamura, M. Small, and Y. Hirata, ``Testing for nonlinearity in irregular fluctuations with long term trends,'' Physical Review E 74, 026205 (2006).

- Y. Hirata, A. Oda, K. Ohta, and K. Aihara, ``Three-dimensional reconstruction of single-cell chromosome structure using recurrence plots,'' Sci. Rep. 6, 34982 (2016).
- Y. Hirata, J. M. Amigo, Y. Matsuzaka, R. Yokota, H. Mushiake, and K. Aihara, ``Detecting causality by combined use of multiple methods: climate and brain examples,'' PLoS One 11, e0158572 (2016).
- K. Iwayama, L. Zhu, Y. Hirata, M. Aono, M. Hara, and K. Aihara, ``Decision-making ability of Physarum polycephalum enhanced by its coordinated spatiotemporal oscillatory dynamics,'' Bioinspiration & Biomimetics 11, 036001 (2016).
- N. Takahashi, Y. Hirata, K. Aihara, and P. Mas, ``A hierarchical multi-oscillator network orchestrates the Arabidopsis circadian system,'' Cell 163, 148-159 (2015).
- A. Oda, N. Takemata, Y. Hirata, T. Miyoshi, Y. Suzuki, S. Sugano, and K. Ohta, ``Dynamic transition of transcription and chromatin landscape during ssion yeast adaptation to glucose starvation,'' Genes Cells 20, 392-407 (2015).
- M. Aono, Y. Hirata, M. Hara, and K. Aihara, ``Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer,'' Nat. Comput. 10, 1219-1244 (2011).
- T. Nakai, Y. Hirata, S. Horai, M. Akagi, and K. Aihara, ``Firm evidence of chaos for heartbeats in dogs under constant flow ventilation,'' Int. J. Bifurcat. Chaos 20, 4151-4158 (2010).
- M. Aono, Y. Hirata, M. Hara, and K. Aihara, ``A model of amoeba-based neurocomputer,'' Journal of Computer Chemistry, Japan 9, 143-156 (2010).
- M. Aono, Y. Hirata, M. Hara, and K. Aihara, ``Amoeba-based chaotic neurocomputing: combinatorial optimization by coupled biological oscillators,'' New Generation Computing 27, 129-157 (2009).

- Y. Hirata, K. Iwayama, and K. Aihara, ``Possibility of short-term probabilistic forecasts for large earthquakes making good use of the limitations of existing catalogs,'' Phys. Rev. E 94, 042217 (2016).
- Y. Hirata, J. M. Amigo, Y. Matsuzaka, R. Yokota, H. Mushiake, and K. Aihara, ``Detecting causality by combined use of multiple methods: climate and brain examples,'' PLoS One 11, e0158572 (2016).
- T. Omi, Y. Ogata, Y. Hirata, and K. Aihara, ``Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches,'' Journal of Geophysical Research: Solid Earth 120, 2561-2578 (2015).
- T. Omi, Y. Ogata, Y. Hirata, and K. Aihara, ``Estimating the ETAS model from early aftershock sequence,'' Geophys. Res. Lett. 41, 850-857 (2014).
- T. Omi, Y. Ogata, Y. Hirata, and K. Aihara, ``Forecasting large aftershocks within one day after the main shock,'' Sci. Rep. 3, 2218 (2013).

- T. Omi, Y. Hirata, and K. Aihara, ``Hawkes process model with a timedependent background rate and its application to high-frequency financial data,'' Phys. Rev. E 96, 012303 (2017)
- Y. Hirata, ``Mathematically modelling proportions of Japanese populations by industry,'' Physica A 460, 38-43 (2016).
- S. Saito, Y. Hirata, K. Sasahara, and H. Suzuki, ``Tracking time evolution of collective attention clusters in Twitter: time evolving nonnegative matrix factorisation,'' PLoS One 10, 0139085 (2015).
- K. Sasahara, Y. Hirata, M. Toyoda, M. Kitsuregawa, and K. Aihara, ``Quantifying collective attention from tweet stream'', PLoS ONE 8, e61823 (2013).

- M. Nagata, Y. Hirata, N. Fujiwara, G. Tanaka, H. Suzuki, K. Aihara, ``Smoothing effect for spatially distributed renewable resources and its impact on power grid robustness'' Chaos 27, 033104 (2017).
- T. Sase, J. Ramirez, K. Kitajo, K. Aihara, and Y. Hirata, ``Estimating the level of dynamical noise in time series by using fractal dimensions,'' Physics Letters A 380, 1151-1163 (2016).
- S. Uenohara, T. Mitsui, Y. Hirata, T. Morie, Y. Horio, and K. Aihara, ``Experimental distinction between chaotic and strange nonchaotic attractors on the basis on consistency'', Chaos 23, 023110 (2013).
- Y. Hirata, M. Aono, M. Hara, and K. Aihara, ``Spontaneous mode switching in coupled oscillators competing for constant amounts of resources,'' Chaos 20, 013117 (2010).
- T. Nakamura, Y. Hirata, K. Judd, D. Kilminster, and M. Small, ``Improved parameter estimation from noisy time series for nonlinear dynamical systems,'' International Journal of Bifurcation and Chaos 17, 1741-1752 (2007).

- M. Fukino, Y. Hirata, and K. Aihara, ``Music visualized by nonlinear time series analysis,'' SIAM News (2016).
- 合原一幸, 平田祥人, 奥牧人, 「複雑系と生命ビッグデータ」 実験医学 34, 84-88 (2016).
- Y. Hirata, K. Morino, T. Suzuki, Q. Guo, H. Fukuhara, and K. Aihara, ``System identification and parameter estimation in mathematical medicine: examples demonstrated for prostate cancer,'' Quantitative Biology 4, 13-19 (2016).
- K. Aihara, and Y. Hirata, "Special section on complex systems modelling and its transdisciplinary applications," NOLTA J. 6, 1-1 (2015).
- 平田祥人, 合原一幸, 外力が加わっているシステムの状態の再構成, 生体の科学 65, 442-443 (2014).
- M. A. Dahlem, S. Rode, A. May, N. Fujiwara, Y. Hirata, K. Aihara, and J. Kurths, ``Towards dynamical network biomarkers in neuromodulation of episodic migraine,'' Transl. Neurosci. 4, 282-294 (2013).
- Y. Hirata, M. Oku, and K. Aihara, ``Chaos in neurons and its application: Perspective of chaos engineering,'' Chaos 22, 047511 (2012).
- 平田祥人, 点過程時系列データの非線形時系列解析，システム/制御/情報 56, 355-360 (2012).
- 平田祥人, 前立腺がん治療の数理モデル，応用数理 22, 27-36 (2012).
- Y. Hirata, K. Akakura, C. S. Higano, N. Brhchovsky, and K. Aihara, ``Quantitative mathematical modeling of PSA dynamics of prostate cancer patients treated with intermittent androgen suppression,'' J. Mol. Cell Biol. 4, 127-132 (2012).
- Y. Hirata, G. Tanaka, N. Brhchovsky, and K. Aihara, ``Mathematically modelling and controlling prostate cancer under intermittent hormone therapy,'' Asian J. Androl. 14, 270-277 (2012).
- 平田祥人, リカレンスプロット：時系列の視覚化を超えて，数理解析研究所講究録 1768, 150-162 (2011).
- 田中剛平, 平田祥人, 山田泰司, 高橋純, 合原一幸, 数理モデルに基づくテーラーメード前立腺癌間歇的内分泌療法, 泌尿器外科 24, 31-38 (2011).

- S. Okuno, T. Takeuchi, S. Horai, K. Aihara, and Y. Hirata, ``Avoiding underestimates for time series prediction by state-dependent local integration,'' Mathematical engineering technical reports METR 2017-22, November 2017.
- Y. Hirata, N. Bruchovsky, and K. Aihara, ``A mathematical model identifies outcome of hormone therapy for prostate cancer,'' Mathematical engineering technical reports METR2008-34, August 2008.
- K. Fukuda, Y. Hirata, and K. Aihara, ``Estimating good local cross sections of expansive flows,'' Mathematical engineering technical reports METR2006-45, July 2006.
- K. Fukuda, Y. Hirata, and K. Aihara, ``Non-generating partitions for surjective tent map,'' Mathematical engineering technical reports METR2006-44, July 2006.
- Y. Hirata, Y. Katori, H. Suzuki, and K. Aihara, ``Testing a neural coding hypothesis using surrogate data,'' Mathematical engineering technical reports METR2005-35, November 2005.
- Y. Hirata, S. Horai, H. Suzuki, and K. Aihara, ``Wayland method can rate some datasets as less deterministic than their random shuffle surrogates,'' Mathematical engineering technical reports METR2005-05, February 2005.