pith. sign in

arxiv: 2606.13285 · v1 · pith:YOJY4DZXnew · submitted 2026-06-11 · 💻 cs.LG · cs.AI

Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation

Beinan Xu , Andy Song , Jiti Gao , Feng Liu This is my paper

Pith reviewed 2026-06-27 07:24 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords simultaneous forecastingequilibrium state estimationmulti-system predictionscalable time seriescoordinated forecastssingle-pass predictionlinear complexity forecasting
0
0 comments X

The pith

Equilibrium State Estimation forecasts multiple interacting systems simultaneously by estimating a shared equilibrium state and deriving all predictions from deviations of current states.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Equilibrium State Estimation as a way to predict many linked systems in one step rather than one at a time. It works by first finding an equilibrium point that the systems would approach together, then using how far each current state sits from that point to produce all forecasts at once. A reader would care if the claim holds because problems like currency markets or epidemic tracking across locations need many coordinated predictions that remain fast even as the number of systems grows. The method is said to match the accuracy of separate top predictors while running 10-70 times faster with linear scaling and staying stable under changes to the data.

Core claim

Equilibrium State Estimation estimates the equilibrium state across systems, then generates holistic forecasts based on the difference between the current state and the estimated equilibrium. This produces separate yet coordinated forecasts for all systems in a single pass, achieving accuracy comparable to state-of-the-art methods with 10-70x speedup, linear-time complexity, and robustness under perturbations.

What carries the argument

Equilibrium State Estimation, which computes a single equilibrium state shared across all systems and then produces forecasts from the deviations of each system's current state from that equilibrium.

If this is right

  • Conventional predictors can be combined with ESE to retain their accuracy while gaining the speed and scalability of the single-pass approach.
  • Forecasting time scales linearly with the number of systems rather than quadratically or worse.
  • Predictions remain reliable even when input data receives diverse perturbations.
  • The method applies directly to domains such as currency exchange rates and COVID-19 spread across multiple locations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Large-scale applications like global financial networks or multi-region climate tracking could shift from sequential to simultaneous forecasting if the equilibrium estimate proves stable.
  • The approach might extend to any setting where separate models are currently run on coupled variables, provided an equilibrium can be defined without additional assumptions.
  • Integration with existing time-series libraries would allow quick replacement of per-system loops with one equilibrium calculation followed by deviation-based outputs.

Load-bearing premise

A meaningful equilibrium state across interacting systems can be estimated such that deviations from the current state yield accurate coordinated forecasts for all systems simultaneously.

What would settle it

A test on a dataset of known interacting systems where the simultaneous forecasts from ESE are less accurate overall than those from separate state-of-the-art predictors run one system at a time.

Figures

Figures reproduced from arXiv: 2606.13285 by Andy Song, Beinan Xu, Feng Liu, Jiti Gao.

Figure 1
Figure 1. Figure 1: Illustration of three systems (A, B, and C) reaching equilibrium under four scenarios. Each system is characterized by the same set of three attributes. The red solid line above A, B, C in each scenario represents the corresponding equilibrium state (E S ). Each point on the line indicates the system’s target value at E S - γ ∗ A, γ∗ B, and γ ∗ C , respectively [* for equilibrium]. Equilibrium Equilibrium … view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the transition between states over time in M S . Each purple dotted line represents the actual state [γA, γB, γC ] at a given time point. The red lines, consistent with those in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Complexity analysis of ESE. (Left): ESE’s cost relative to the number of regions and the number of days for COVID-19 data (with 9 attributes). (Right): ESE’s cost relative to the number of regions and the number of attributes for COVID-19 (input size = 150). the number of attributes, making the forecasting of multiple systems scalable. Robustness ESE simultaneously predicts all systems in M S , making it r… view at source ↗
Figure 4
Figure 4. Figure 4: Convergence of ESE on Synthetic Data, 20 Systems. The blue line represents the p-values obtained at each epoch of ESE convergence. The red dotted line represents a p-value of 0.05, the threshold for rejecting the null hypothesis for the existence of a long-run equilibrium [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Convergence of ESE on COVID-19 Data, 79 Systems. The blue line represents p-values obtained from the cointegration test at each epoch of ESE convergence. The red dotted line is the threshold for rejecting the null hypothesis for the existence of a long-run equilibrium. p-value 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14 0.15 RMSE 5.87 5.68 6.04 5.88 6.01 6.58 7.41 6.95 7.68 8.38 7… view at source ↗
Figure 6
Figure 6. Figure 6: Comparing with 13 SOTA methods in RMSE on different numbers (20, 79 and 320) of systems for COVIDE-19 data (input size = 50, horizon = 1 ) [PITH_FULL_IMAGE:figures/full_fig_p033_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparing with 13 SOTA methods in RMSE on different input sizes, 10, 20, 50 and 100 (79 regions, horizon = 1 ) [PITH_FULL_IMAGE:figures/full_fig_p034_7.png] view at source ↗
read the original abstract

We introduce Equilibrium State Estimation (ESE), a novel paradigm for simultaneous prediction, where multiple interacting systems require separate yet coordinated forecasts. Such scenarios often arise in real-world settings such as economics and healthcare modeling. Unlike existing approaches that predict one system at a time, ESE forecasts all systems in a single pass. It first estimates the equilibrium state across systems, then generates holistic forecasts based on the difference between the current state and the estimated equilibrium. Extensive experiments on synthetic and real-world datasets, including currency exchange and COVID-19 spread modeling, demonstrate that ESE is at least as accurate as state-of-the-art (SOTA) methods while being significantly faster. In addition, ESE integrates seamlessly with conventional predictors, combining their accuracy with its exceptional efficiency and delivering a 10-70x speedup. With linear-time complexity, ESE scales far better than SOTA methods as the number of systems increases. Moreover, it remains accurate under diverse perturbations, establishing ESE as a fast, generalizable, robust, and scalable multi-prediction method.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces Equilibrium State Estimation (ESE) for simultaneous forecasting across multiple interacting systems. It estimates a single equilibrium state for all systems and produces coordinated forecasts from the deviations between current observations and this equilibrium. The method is claimed to match state-of-the-art accuracy on synthetic data, currency exchange rates, and COVID-19 case modeling while delivering 10-70x speedups, linear-time complexity, robustness to perturbations, and seamless integration with existing predictors.

Significance. If the central mechanism and reported speed/accuracy results hold under scrutiny, ESE would address a genuine scalability bottleneck in multi-system forecasting tasks common to economics and epidemiology. The linear complexity and single-pass property represent a potentially useful engineering advance over per-system sequential methods.

major comments (2)
  1. [Abstract] Abstract: the claim of 'extensive experiments on synthetic and real-world datasets' demonstrating accuracy, speedup, and robustness is unsupported by any presented methodology, quantitative tables, baselines, error bars, or statistical tests, preventing evaluation of the central accuracy and speedup assertions.
  2. [Abstract] Abstract: the equilibrium-state estimation step is described only at the level of 'estimates the equilibrium state across systems'; without the explicit procedure (e.g., optimization objective, closed-form solution, or learned parameters), it is impossible to determine whether the subsequent deviation-based forecasts are non-circular or genuinely predictive rather than tautological.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback on the abstract. We address each major comment below and will revise the abstract accordingly to improve clarity and support for the claims while preserving its concise nature.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of 'extensive experiments on synthetic and real-world datasets' demonstrating accuracy, speedup, and robustness is unsupported by any presented methodology, quantitative tables, baselines, error bars, or statistical tests, preventing evaluation of the central accuracy and speedup assertions.

    Authors: We agree that the abstract summarizes results at a high level without embedding the full experimental details. The manuscript contains a dedicated Experiments section presenting methodology, quantitative tables comparing against baselines on synthetic data, currency exchange rates, and COVID-19 modeling, along with speedup measurements (10-70x), error bars, and robustness tests under perturbations. To make the abstract's claims more self-contained and directly evaluable, we will revise it to include concise references to key quantitative outcomes and the validation approach. revision: yes

  2. Referee: [Abstract] Abstract: the equilibrium-state estimation step is described only at the level of 'estimates the equilibrium state across systems'; without the explicit procedure (e.g., optimization objective, closed-form solution, or learned parameters), it is impossible to determine whether the subsequent deviation-based forecasts are non-circular or genuinely predictive rather than tautological.

    Authors: The abstract provides an intentionally concise overview. The full manuscript details the equilibrium state estimation procedure, including the specific optimization objective or closed-form solution used to compute the shared equilibrium from which deviations yield the forecasts. This ensures the forecasts are non-circular and predictive. To address the concern directly in the abstract, we will expand the method description slightly to note that the equilibrium is estimated via [brief reference to the procedure] and forecasts derive from observed deviations, clarifying the predictive nature. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided abstract and description outline ESE as estimating an equilibrium state then forecasting from deviations, with experiments on synthetic, currency, and COVID data plus integration with base predictors. No equations, self-citations, or derivation steps are quoted that reduce any prediction to a fitted input or self-definition by construction. The central mechanism is presented as an independent estimation procedure whose outputs are validated externally against SOTA accuracy and speed, with no load-bearing self-referential definitions or renamed known results visible. This is the common honest outcome for a method paper whose claims rest on empirical benchmarks rather than internal algebraic closure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Review based solely on abstract; no details on parameters, assumptions, or entities available.

invented entities (1)
  • Equilibrium state no independent evidence
    purpose: Core estimation target enabling simultaneous forecasts across systems
    Introduced as the key mechanism in ESE without external validation or falsifiable handle mentioned in abstract.

pith-pipeline@v0.9.1-grok · 5713 in / 1235 out tokens · 35607 ms · 2026-06-27T07:24:22.625987+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

219 extracted references · 9 canonical work pages · 2 internal anchors

  1. [1]

    Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , pages=

    All models are useful: Bayesian ensembling for robust high resolution covid-19 forecasting , author=. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , pages=

  2. [2]

    2020 International conference on smart electronics and communication (ICOSEC) , pages=

    Regression analysis of COVID-19 using machine learning algorithms , author=. 2020 International conference on smart electronics and communication (ICOSEC) , pages=. 2020 , organization=

    2020

  3. [3]

    The Journal of Infection in Developing Countries , volume=

    Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis , author=. The Journal of Infection in Developing Countries , volume=

  4. [4]

    Regional Science Policy & Practice , volume=

    Spatial distribution dynamics and prediction of COVID-19 in Asian countries: Spatial Markov chain approach , author=. Regional Science Policy & Practice , volume=. 2020 , publisher=

    2020

  5. [5]

    Science of The Total Environment , volume=

    Prediction for the spread of COVID-19 in India and effectiveness of preventive measures , author=. Science of The Total Environment , volume=. 2020 , publisher=

    2020

  6. [6]

    International Journal of Urban Sciences , volume=

    COVID-19 impact on city and region: what’s next after lockdown? , author=. International Journal of Urban Sciences , volume=. 2020 , publisher=

    2020

  7. [7]

    Ieee Access , volume=

    Controlling the outbreak of COVID-19: A noncooperative game perspective , author=. Ieee Access , volume=. 2020 , publisher=

    2020

  8. [8]

    Nonlinear Analysis and Global Optimization , pages=

    Competition for medical supplies under stochastic demand in the Covid-19 pandemic: A generalized nash equilibrium framework , author=. Nonlinear Analysis and Global Optimization , pages=. 2021 , publisher=

    2021

  9. [9]

    SIAM Journal on Computing , volume=

    The complexity of computing a Nash equilibrium , author=. SIAM Journal on Computing , volume=. 2009 , publisher=

    2009

  10. [10]

    Mathematical Modelling of Natural Phenomena , volume=

    Contact rate epidemic control of COVID-19: an equilibrium view , author=. Mathematical Modelling of Natural Phenomena , volume=. 2020 , publisher=

    2020

  11. [11]

    Statistical Mechanics of Membranes and Surfaces , pages=

    Equilibrium Statistical Mechanics , author=. Statistical Mechanics of Membranes and Surfaces , pages=. 2004 , publisher=

    2004

  12. [12]

    Open Journal of Biophysics , volume=

    On the dynamic equilibrium in homeostasis , author=. Open Journal of Biophysics , volume=. 2012 , publisher=

    2012

  13. [13]

    Digital Government: Research and Practice , volume=

    Spreading of COVID-19 in India, Italy, Japan, Spain, UK, US: a prediction using ARIMA and LSTM model , author=. Digital Government: Research and Practice , volume=. 2020 , publisher=

    2020

  14. [14]

    Chaos, Solitons & Fractals , volume=

    A SIR model assumption for the spread of COVID-19 in different communities , author=. Chaos, Solitons & Fractals , volume=. 2020 , publisher=

    2020

  15. [15]

    World Journal of Modelling and Simulation , volume=

    A SIR model for spread of dengue fever disease (simulation for South Sulawesi, Indonesia and Selangor, Malaysia) , author=. World Journal of Modelling and Simulation , volume=. 2013 , publisher=

    2013

  16. [16]

    Chaos, Solitons & Fractals , volume=

    Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM , author=. Chaos, Solitons & Fractals , volume=. 2020 , publisher=

    2020

  17. [17]

    Data in brief , volume=

    Application of the ARIMA model on the COVID-2019 epidemic dataset , author=. Data in brief , volume=. 2020 , publisher=

    2019

  18. [18]

    SSM-population health , volume=

    The impact of Covid-19, associated behaviours and policies on the UK economy: A computable general equilibrium model , author=. SSM-population health , volume=. 2020 , publisher=

    2020

  19. [19]

    African Development Review , volume=

    The impacts of the Covid-19 pandemic on employment in Cameroon: A general equilibrium analysis , author=. African Development Review , volume=. 2021 , publisher=

    2021

  20. [20]

    Virtual Macro Seminar , pages=

    The behavioral SIR model, with applications to the Swine Flu and COVID-19 pandemics , author=. Virtual Macro Seminar , pages=

  21. [21]

    Covid Economics (forthcoming) , year=

    Nash SIR: an economic-epidemiological model of strategic behavior during a viral epidemic , author=. Covid Economics (forthcoming) , year=

  22. [22]

    Nature , volume=

    Population flow drives spatio-temporal distribution of COVID-19 in China , author=. Nature , volume=. 2020 , publisher=

    2020

  23. [23]

    J Geogr Stud , volume=

    Distribution and trend analysis of COVID-19 in India: geospatial approach , author=. J Geogr Stud , volume=

  24. [24]

    Spatial analysis of the COVID-19 distribution pattern in S

    Rex, Franciel Eduardo and Borges, Cl. Spatial analysis of the COVID-19 distribution pattern in S. Ci. 2020 , publisher=

    2020

  25. [25]

    Human resources for health , volume=

    Measuring inequalities in the distribution of health workers: the case of Tanzania , author=. Human resources for health , volume=. 2009 , publisher=

    2009

  26. [26]

    Applied Geography , volume=

    Inequality of public health and its role in spatial accessibility to medical facilities in China , author=. Applied Geography , volume=. 2018 , publisher=

    2018

  27. [27]

    Globalization and health , volume=

    The development and reform of public health in China from 1949 to 2019 , author=. Globalization and health , volume=. 2019 , publisher=

    1949

  28. [28]

    Obesity reviews , volume=

    Should patients with obesity be more afraid of COVID-19? , author=. Obesity reviews , volume=. 2020 , publisher=

    2020

  29. [29]

    Obesity Reviews , volume=

    Individuals with obesity and COVID-19: a global perspective on the epidemiology and biological relationships , author=. Obesity Reviews , volume=. 2020 , publisher=

    2020

  30. [30]

    The lancet respiratory medicine , volume=

    Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? , author=. The lancet respiratory medicine , volume=. 2020 , publisher=

    2020

  31. [31]

    JMIR public health and surveillance , volume=

    The association between chronic disease and serious COVID-19 outcomes and its influence on risk perception: survey study and database analysis , author=. JMIR public health and surveillance , volume=. 2021 , publisher=

    2021

  32. [32]

    Frontiers in medicine , volume=

    Age-dependent risks of incidence and mortality of COVID-19 in Hubei Province and other parts of China , author=. Frontiers in medicine , volume=. 2020 , publisher=

    2020

  33. [33]

    Applied Mathematics and Computation , volume=

    A numerical study on an influenza epidemic model with vaccination and diffusion , author=. Applied Mathematics and Computation , volume=. 2012 , publisher=

    2012

  34. [34]

    Geophysical Research Letters , volume=

    Isostatic equilibrium in spherical coordinates and implications for crustal thickness on the Moon, Mars, Enceladus, and elsewhere , author=. Geophysical Research Letters , volume=. 2017 , publisher=

    2017

  35. [35]

    2018 , publisher=

    Forecasting: principles and practice , author=. 2018 , publisher=

    2018

  36. [36]

    2022 , publisher=

    Quantitative Financial Modelling Framework , author=. 2022 , publisher=

    2022

  37. [37]

    1934 , publisher=

    Security analysis , author=. 1934 , publisher=

    1934

  38. [38]

    The review of asset pricing studies , volume=

    The unprecedented stock market reaction to COVID-19 , author=. The review of asset pricing studies , volume=. 2020 , publisher=

    2020

  39. [39]

    Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 , author=. Eur. J. Oper. Res. , year=

  40. [40]

    The Journal of Supercomputing , volume=

    An innovative neural network approach for stock market prediction , author=. The Journal of Supercomputing , volume=. 2020 , publisher=

    2020

  41. [41]

    Proceedings of the 2019 ACM Conference on Economics and Computation , pages=

    Computing large market equilibria using abstractions , author=. Proceedings of the 2019 ACM Conference on Economics and Computation , pages=

    2019

  42. [42]

    Proceedings of the 2016 ACM Conference on Economics and Computation , pages=

    Large market games with near optimal efficiency , author=. Proceedings of the 2016 ACM Conference on Economics and Computation , pages=

    2016

  43. [43]

    Proceedings of the 2019 ACM Conference on Economics and Computation , pages=

    Regression equilibrium , author=. Proceedings of the 2019 ACM Conference on Economics and Computation , pages=

    2019

  44. [44]

    Finance, Markets and Valuation , volume=

    Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks , author=. Finance, Markets and Valuation , volume=. 2020 , publisher=

    2020

  45. [45]

    2021 IEEE International Conference on Big Data (Big Data) , pages=

    Forecasting high-risk areas of covid-19 infection through socioeconomic and static spatial analysis , author=. 2021 IEEE International Conference on Big Data (Big Data) , pages=. 2021 , organization=

    2021

  46. [46]

    Alexandria Engineering Journal , year=

    Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends , author=. Alexandria Engineering Journal , year=

  47. [47]

    Applied Soft Computing , year=

    Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) , author=. Applied Soft Computing , year=

  48. [48]

    Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia , author=. Complex. , year=

  49. [49]

    IEEE Transactions on Instrumentation and Measurement , year=

    EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases , author=. IEEE Transactions on Instrumentation and Measurement , year=

  50. [50]

    Soft Computing , year=

    India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability , author=. Soft Computing , year=

  51. [51]

    The European Journal of Health Economics , year=

    Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy , author=. The European Journal of Health Economics , year=

  52. [52]

    Universal Journal of Mathematics and Applications , year=

    Forecasting COVID-19 disease cases using the SARIMA-NNAR hybrid model , author=. Universal Journal of Mathematics and Applications , year=

  53. [53]

    Applied Sciences , year=

    Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM , author=. Applied Sciences , year=

  54. [54]

    ArXiv , year=

    AR-Net: A simple Auto-Regressive Neural Network for time-series , author=. ArXiv , year=

  55. [55]

    KSCE Journal of Civil Engineering , year=

    Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Influent Characteristics , author=. KSCE Journal of Civil Engineering , year=

  56. [56]

    Personal and Ubiquitous Computing , year=

    Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks , author=. Personal and Ubiquitous Computing , year=

  57. [57]

    , author=

    Data driven time-varying SEIR-LSTM/GRU algorithms to track the spread of COVID-19. , author=. Mathematical biosciences and engineering : MBE , year=

  58. [58]

    Computational and Mathematical Methods in Medicine , year=

    Forecasting COVID-19 Pandemic Using Prophet, ARIMA, and Hybrid Stacked LSTM-GRU Models in India , author=. Computational and Mathematical Methods in Medicine , year=

  59. [59]

    Applied Sciences , year=

    Predictability of COVID-19 Infections Based on Deep Learning and Historical Data , author=. Applied Sciences , year=

  60. [60]

    Acta Bio Medica : Atenei Parmensis , year=

    WHO Declares COVID-19 a Pandemic , author=. Acta Bio Medica : Atenei Parmensis , year=

  61. [61]

    , author=

    Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. , author=. JAMA , year=

  62. [62]

    Archives of Academic Emergency Medicine , year=

    Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis , author=. Archives of Academic Emergency Medicine , year=

  63. [63]

    PLoS ONE , year=

    Prevalence of underlying diseases in died cases of COVID-19: A systematic review and meta-analysis , author=. PLoS ONE , year=

  64. [64]

    Frontiers in Physiology , year=

    Aging, Immunity, and COVID-19: How Age Influences the Host Immune Response to Coronavirus Infections? , author=. Frontiers in Physiology , year=

  65. [65]

    Advances in Neural Information Processing Systems , volume=

    Deep equilibrium models , author=. Advances in Neural Information Processing Systems , volume=

  66. [66]

    Advances in Neural Information Processing Systems , volume=

    Multiscale deep equilibrium models , author=. Advances in Neural Information Processing Systems , volume=

  67. [67]

    Game Theory , pages=

    Nash equilibrium , author=. Game Theory , pages=. 1989 , publisher=

    1989

  68. [68]

    Australasian Joint Conference on Artificial Intelligence , pages=

    Modelling Eye-Gaze Movement Using Gaussian Auto-regression Hidden Markov , author=. Australasian Joint Conference on Artificial Intelligence , pages=. 2022 , organization=

    2022

  69. [69]

    Principles of epidemiology in public health practice; an introduction to applied epidemiology and biostatistics , author=

  70. [70]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  71. [71]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Equilibrium Learning in Auction Markets , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  72. [72]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Model-free online learning in unknown sequential decision making problems and games , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  73. [73]

    Using stacking to average Bayesian predictive distributions (with discussion) , author=

  74. [74]

    Journal of economic structures , volume=

    Does financial inclusion reduce poverty and income inequality in developing countries? A panel data analysis , author=. Journal of economic structures , volume=. 2020 , publisher=

    2020

  75. [75]

    Technological Forecasting and Social Change , volume=

    The impact of innovation on economic growth among G7 and BRICS countries: A GMM style panel vector autoregressive approach , author=. Technological Forecasting and Social Change , volume=. 2021 , publisher=

    2021

  76. [76]

    2022 , publisher=

    Analysis of panel data , author=. 2022 , publisher=

    2022

  77. [77]

    Earth's Future , volume=

    Equilibrium modeling for environmental science: Exploring the nexus of economic systems and environmental change , author=. Earth's Future , volume=. 2021 , publisher=

    2021

  78. [78]

    Annals of tourism research , volume=

    Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak , author=. Annals of tourism research , volume=. 2020 , publisher=

    2020

  79. [79]

    IEEE Transactions on Computational Social Systems , volume=

    A hybrid approach of Bayesian structural time series with lstm to identify the influence of news sentiment on short-term forecasting of stock price , author=. IEEE Transactions on Computational Social Systems , volume=. 2021 , publisher=

    2021

  80. [80]

    CREATES Research paper , volume=

    Glossary to arch (garch) , author=. CREATES Research paper , volume=

Showing first 80 references.