Library · Simulation & modelling
What Time Series Forecasting & State-Space got wrong, from 62 theses
Summary written by a model from the records below: The records describe empirical failures and model rejections across statistical, state space, and machine learning methods for time series forecasting. Many studies demonstrate that overly complex or mis-specified models fail to outperform simple baselines when handling non-stationarity, limited historical observations, or high volatility. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 10 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Non-stationarity and improper data adjustments distort time series estimation
Models fail when series exhibit unit roots, non-stationary correlation dynamics, or severe multicollinearity that distorts underlying trends. Inappropriate data treatments like temporal averaging, mean imputation under censoring, and outlier omissions lead to systematic bias and inflated error rates.
Tried and failed
temporal averaging of non-linear input variables applied to wind power generation forecasting. Outcome: worse than baseline. Reason: averaging distorts the skewed distribution shape and severely underestimates non-linear cubic power transformations
Multi-decadal wind power forecasting under climate change · Tübingen · PhD
Tried and failed
threshold-based alerting on continuous time-series data applied to continuous physiological vital sign monitoring. Reason: Excessive false-positive alert rates due to high sensor noise and normal physiological fluctuations.
Characterising clinically integrated remote sensing and digital alerting tools for the assessment of health status in community and hospital care · Imperial · PhD
Tried and failed
mean demand imputation under unobserved censoring applied to competitive market demand forecasting. Outcome: worse than baseline. Reason: Assuming mean market arrivals when undercut caused systematic demand overestimation and severe performance drops
Market-Based and Policy-Based Conditional Demand Forecaster for Airline Revenue Management · MIT · PhD
Tried and failed
clustering-based ensemble forecasting applied to intermittent retail demand forecasting. Outcome: did not generalise. Reason: sparse zero-sales data points and intermittent demand in low-sales regions degraded performance
Advanced Data-Driven Methodologies for Enhanced Demand Forecasting in Supply Chain Management · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected OLS, Fixed Effects, and Difference-GMM estimators due to severe weak instrument bias and failure to address dynamic panel endogeneity in persistent growth time series.
Foreign Investment and Institutional Effectiveness in Resource-Dependent Economies · University at Buffalo · PhD
Tried and failed
time-series forecasting on correlation dynamics applied to predicting non-stationary feature relationships. Outcome: did not generalise. Reason: direct forecasting on multivariate correlation time series failed to capture non-stationary dynamics
Robust machine learning methods for high-dimensional datasets with applications in genomics and finance · Imperial · PhD
Tried and failed
Augmented Dickey-Fuller stationarity testing applied to macroeconomic time series indicators. Reason: Input variables exhibited unit roots and failed to demonstrate stationarity under unit root testing
An Assessment of EV Adoption and Potential Growth under Evolving Techno-policy Scenarios · Georgia Tech · PhD
Tried and failed
standard random forest without linear components applied to persistent macroeconomic time series forecasting. Outcome: did not generalise. Reason: cannot efficiently capture persistent autoregressive dynamics and smooth linear trends in finite samples
Machine Learning Econometrics · Penn · PhD
Tried and failed
omitting outlier periods and using standard pre-adjustments applied to time series macroeconomic forecasting. Reason: leads to statistical mis-specification, causing severe overprediction and underestimated uncertainty
Macroeconomic Forecasting: Statistically Adequate, Temporal Principal Components · Virginia Tech · Doctorate
Lost to a baseline
K-Nearest Neighbors underperformed simpler models (test RMSE of 1.72 °C for summer and 0.84 °C for winter) due to inability to handle time-series trends.
Supply Chain Modeling for Temperature-Sensitive Pharmaceutical Goods · MIT · Master's
Considered and rejected
Considered and rejected: Standard stationary time series assumptions rejected due to nonstationarity and long-range dependence in regressors
Essays on Time-Varying Models in Time Series and Machine Learning · Leibniz Universität Hannover · PhD
Considered and rejected
Considered and rejected: Rejected running time-series regressions on raw levels of log aggregate payrolls and manufacturing employment due to non-stationarity and spurious regression risk.
Altitude Sickness: Comparing the Political Economy of Two Periods of Extreme Dollar Strength · Harvard · Master's
Ignoring spatial network topology and physical states degrades forecasting performance
Standalone time series models struggle when dynamics depend on network connectivity, spatial dependencies, or physical domain variables. Omitting downstream spatial bottlenecks, atmospheric covariates, or coordinate constraints results in over-smoothed forecasts and elevated prediction error.
Tried and failed
pure simulation-trained hybrid neural network forecasting applied to offshore wind speed prediction. Outcome: worse than baseline. Reason: lacking real-world observational ground-truth data fusion during training led to high prediction error against actual measurements
Data-driven and physics-informed wind prediction in offshore wind farms · Nottingham · PhD
Lost to a baseline
Full-physics/ECMWF operational numerical weather models outperform standalone simple ML/CNN formulations for full forecasting.
Machine Learning for Improvement of Ocean Data Resolution for Weather Forecasting and Climatological Research · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Rejected non-graph classical time-series methods (ARIMA, Kalman filters, standard linear regression) because they fail to simultaneously capture dynamic spatial topological features and non-spatial edge throughput/bandwidth constraints.
Using machine learning for dynamic resource orchestration & task scheduling, in a radio access network based edge environment · Oxford · PhD
Considered and rejected
Considered and rejected: Rejected univariate time series models (e.g., ARIMA) for mobility network prediction due to their inability to model cross-series dependencies
Towards Equitable Access to Essential Services: A Data-Driven Human Mobility Modeling Framework · University at Buffalo · PhD
Considered and rejected
Considered and rejected: Rejected linear regression and classical ML models (e.g., ElasticNet R²=0.086) for digital twin energy forecasting due to poor handling of temporal dependencies.
Towards Inclusivity In Building Performance Simulation A Multimodal Calibration Framework For Disadvantaged Communities · Georgia Tech · PhD
Tried and failed
binary classification of precursor states from sensor time-series applied to freeway traffic congestion prediction. Outcome: did not generalise. Reason: sensor coverage omitted the downstream bottleneck location, conflating bottleneck activation with backward shockwave propagation
Identification of Traffic Congestion Precursors using Machine Learning Approaches · Georgia Tech · PhD
Tried and failed
time gating in recurrent graph neural networks applied to spatiotemporal traffic forecasting. Outcome: worse than baseline. Reason: time gating fails to capture large spatial correlations across nodes
Machine Learning On Large-Scale Graphs · Penn · PhD
Considered and rejected
Considered and rejected: Rejected standard continuous-coordinate time-series forecasting (vector regression) because it fails to enforce spatial network continuity and is susceptible to GPS noise.
Deep Generative Models for Trajectory Prediction and Mobility Network Forecasting · York University · Master's
Considered and rejected
Considered and rejected: Rejected standard matrix/tensor decomposition alone for mobility forecasting due to its inability to capture dynamic temporal evolution without temporal regularization/prediction modules
Towards Equitable Access to Essential Services: A Data-Driven Human Mobility Modeling Framework · University at Buffalo · PhD
Tried and failed
black-box spatiotemporal forecasting without physical atmospheric state applied to photovoltaic power generation time series. Reason: models produced over-smoothed predictions that failed to capture abrupt, dynamic cloud-induced ramps without atmospheric covariates
Network time series forecasting in photovoltaics power production · EPFL · PhD
Considered and rejected
Considered and rejected: Rejected simulating range dependence using discrete mooring time series alone, which misses wave-induced horizontal spatial variability along the propagation path.
CHARACTERIZATION OF INTERNAL WAVES AND TIDES OVER A CALDERA IN THE NEW ENGLAND SEAMOUNT CHAIN AND THEIR IMPACT ON SHORT-TERM ACOUSTIC PATH AVAILABILITY · Naval Postgraduate School · Master's
Classical linear models fail to capture intermittency and non-linear dynamics
Linear autoregressive and static statistical models degrade across long horizons, large datasets, and volatile environmental patterns. They fail to represent conditional heteroskedasticity, intermittent occurrences, and evolving seasonal components without non-linear adaptations.
Tried and failed
ARIMA time series forecasting applied to large-scale wind speed prediction. Outcome: did not generalise. Reason: linear autoregressive models degrade significantly over large datasets and long forecast horizons
Wind Speed Prediction using Classical Time Series and Machine Learning Models: A Comparative Analysis · Texas Tech · PhD
Tried and failed
classical time series and statistical forecasting models applied to wind speed prediction. Outcome: did not generalise. Reason: unable to capture intermittent behavior and non-linearity over long-term forecasting horizons or large datasets
Wind Speed Prediction using Classical Time Series and Machine Learning Models: A Comparative Analysis · Texas Tech · PhD
Tried and failed
linear time series models without conditional heteroskedasticity applied to intermittent wind power generation forecasting. Outcome: did not generalise. Reason: unable to capture conditional volatility and intermittency without variance modeling components
Forecasting of wind power generation using wind speed and temperature for thambapawani wind farm in Sri Lanka · University of Moratuwa · Master's
Tried and failed
single-model distribution forecasting applied to long-term wind speed forecasting. Outcome: worse than baseline. Reason: produced smaller standard deviation and poorer fit compared to Bayesian Model Averaging
Decommissioning strategy to reduce the cost and risk-driving factors in the offshore wind industry. · Cranfield · PhD
Tried and failed
ARIMA and ARIMAX statistical time-series forecasting applied to direct normal irradiance solar forecasting. Outcome: no signal. Reason: linear statistical models failed to capture variance during foggy, highly volatile atmospheric conditions
Machine learning applications for the optimization of renewable energy systems · Iowa State · PhD
Tried and failed
seasonal autoregressive integrated moving average imputation applied to wind speed time series data. Outcome: worse than baseline. Reason: Produced significant distributional distortion and high KL divergence compared to non-seasonal ARIMA and LSTM
Data Driven Early Stage Design Support for Offshore Wind Farms · University College Dublin · PhD
Considered and rejected
Considered and rejected: Rejected SARIMA for long-cycle seasonal forecasting due to computational inefficiency over long periods, using Fourier terms with ARIMA/Ridge instead
A Novel Computational Ethology Framework for Studying Animal Behaviour Under Climate Change (CEFABC2): A Case Study of Little Penguins on Phillip Island · York University · Master's
Considered and rejected
Considered and rejected: Rejected classic linear time series forecasting models (like ARIMA) due to linearity assumptions and inability to handle non-linear multi-target attributes efficiently.
Individual trustworthiness analysis on social media · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected non-ML-based forecasting methods (ARIMA and exponential smoothing) as baselines because they struggle to effectively capture complex patterns and irregularities with non-linear trends
Machine learning-based performance analytics for high-performance computing systems · Boston University · PhD
Highly parameterized and time-varying models underperform parsimonious baselines
Complex approaches such as time-varying vector autoregressions, regime conditioning, and Bayesian synthesis often lose to simpler invariant models or random walks. In several forecasting benchmarks, naive rules and low-order autoregressions achieve lower prediction errors than heavily parameterized alternatives.
Lost to a baseline
ARIMA achieved 1.0 standardized RMSE on Solar time series where Bayesian Synthesis achieved 1.47
Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs · MIT · PhD
Lost to a baseline
Naive forecasting method outperformed ARIMA(1,1,0) at k=1 step-ahead forecasting in Lab Study 1 (median MASE = 1.10).
A Graph-Time Signal Processing Approach for Modeling and Monitoring with Applications in Healthcare and Occupational Safety · University at Buffalo · PhD
Lost to a baseline
Nelson-Siegel-Svensson model achieved higher RMSE (worse forecasting performance) than the simpler Nelson-Siegel (Diebold & Li) model in rolling window AR(1) forecasts.
Estimating and forecasting the yield curve : Sri Lankan government securities market · University of Moratuwa · Master's
Tried and failed
Regime-dependent state conditioning in predictive regression applied to text sentiment market forecasting. Outcome: worse than baseline. Reason: Conditioning on recession states statistically significantly increased forecast errors rather than improving predictability
Market-level and firm-level implications of financial distress · Imperial · Doctorate
Lost to a baseline
In small-scale 1-quarter point forecasting for GDP deflator, time-invariant VAR-MN achieved a lower RMSFE (0.9443) than the TV-SV-VAR-MN benchmark.
Bayesian Inference with Applications to Macroeconomics and Financial Market Price Discovery · University of Washington · PhD
Lost to a baseline
In small-scale 1-quarter point forecasting for Real GDP, standard flat VAR-OLS (0.9544) and VAR-MN (0.7442) outperformed the TV-SV-VAR-MN benchmark.
Bayesian Inference with Applications to Macroeconomics and Financial Market Price Discovery · University of Washington · PhD
Lost to a baseline
The Acemoglu et al. (2016) innovation network model was vastly outperformed in patent activity forecasting by a simple geometric random walk with drift.
Network-dependent dynamics of innovation and production · Oxford · PhD
Data sparsity and short historical samples lead to estimation breakdown
Recurrent networks and multivariate models suffer from severe overfitting and non-positive definite covariance matrices when applied to short time series. Coarse temporal resolutions and limited observation counts leave insufficient longitudinal points for stable parameter estimation.
Tried and failed
feature extraction and regression on short time-series applied to predicting skill demand trends. Outcome: worse than baseline. Reason: noise in short time-series made complex models underperform simple heuristic thresholds
Detecting Latent Training Needs Using Large Datasets · EPFL · PhD
Tried and failed
recurrent neural network time series forecasting applied to single-metric voter turnout estimation. Outcome: data insufficient. Reason: insufficient historical data points across election cycles to train time series models effectively
Exploring social and economic predictors for U.S. Government elections · Texas Tech · PhD
Tried and failed
regression and statistical performance trend modeling applied to time-series performance under new competition formats. Outcome: data insufficient. Reason: insufficient historical data points existed following a major regulatory and format change
Evaluating the Impact of Equipment Investments on Olympic Medal Probabilities for Australian Professional Cyclists · MIT · Master's
Considered and rejected
Considered and rejected: Rejected deep neural networks as primary ML models in sparse time-series experiments due to insufficient data volume for tuning complex architectures.
Beyond predictions: alignment between prior knowledge and machine learning for human-centric augmented intelligence · Leibniz Universität Hannover · PhD
Considered and rejected
Considered and rejected: Rejected using sole quantitative/econometric regression models as the primary evaluation method because the sample size (n=6) and available time series (T=4-13) were insufficient due to data scarcity.
Evaluation of Global Business Services Centers impact on macroeconomic indicators in Central and Eastern Europe countries Globalių verslo paslaugų centrų poveikio Rytų ir Centrinės Europos šalių makroekonominiams rodikliams vertinimas · Mykolas Romeris University · PhD
Considered and rejected
Considered and rejected: Rejected forecasting future procedural trends from HES time series data because the time series lacked stationarity and sufficient longitudinal timepoints
Iliac vein stenting in the management of chronic venous outflow obstruction · Imperial · PhD
Tried and failed
multivariate time series models applied to macro forecasting with small sample size. Outcome: data insufficient. Reason: small sample size caused non-positive definite covariance errors and severe overfitting
An Assessment of EV Adoption and Potential Growth under Evolving Techno-policy Scenarios · Georgia Tech · PhD
Tried and failed
LSTM multi-step time series forecasting applied to multi-month ahead streamflow prediction. Outcome: data insufficient. Reason: insufficient monthly training samples led to poor long-horizon performance (NSE <= 0 beyond one month)
Development of generative adversarial networks for spatiotemporal fluid flow, atmospheric and flood predictions · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected VECM and GARCH models for stock forecasting because weekly portfolio series were stationary by construction and the 150-week in-sample series was too short for GARCH parameter stability.
Three essays on applied economics with high-frequency consumer data · Nottingham · PhD
Deep neural networks underperform simpler statistical and tabular methods
Recurrent networks and transformers frequently fail to justify their complexity and are beaten by naive, autoregressive, or support vector baselines. In settings with limited sample sizes or severe non-linearity, deep architectures suffer from convergence issues, overfitting, and high predictive variance.
Tried and failed
time series transformer applied to asset price forecasting. Outcome: worse than baseline. Reason: underperformed simpler autoregressive baseline on predictive accuracy metrics
An Economic Evaluation of NFTs: A Systematic Framework for Prediction and Analysis with Other Comparable Assets · Texas Tech · PhD
Tried and failed
ARIMA and recurrent neural networks applied to transient emission time series prediction. Outcome: worse than baseline. Reason: severe non-linearity and limited training sample size caused overfitting and high variance compared to gradient boosting
Tried and failed
hybrid statistical linear model and neural network applied to short-term wind speed forecasting. Outcome: worse than baseline. Reason: non-linear neural layer added complexity without exceeding linear autocorrelation baseline accuracy
Integrating Machine Learning and Weather Analytics for Sizing Variable Generation with Utility-Scale Energy Storage · Texas State University · PhD
Lost to a baseline
LSTM model underperformed basic statistical and naive models in long-term annual milk supply forecasting (MASE 4.96 and MAE 4.8M vs MASE 1.00 and MAE 0.97M for Seasonal Naive).
Post-Hoc, Contrastive, Explainable Artificial Intelligence for Time Series and Image Data · University College Dublin · PhD
Lost to a baseline
For long-term 1-day forecasting, standard SVR (MAE 0.97, MAPE 9.15%) outperformed univariate LSTM (MAE 4.16, MAPE 51.48%) and WEBSEC (MAE 3.14, MAPE 67.99%) in South Plains TX.
Wind Speed Prediction using Classical Time Series and Machine Learning Models: A Comparative Analysis · Texas Tech · PhD
Considered and rejected
Considered and rejected: Rejected artificial neural networks (ANNs) and support vector machines (SVMs) for econometric forecasting because literature concluded they either underperformed ARIMA or did not justify added complexity.
Considered and rejected
Considered and rejected: Machine learning time series forecasters (ANNs, SVMs), rejected because statistical models matched or exceeded accuracy with lower computational overhead and less overfitting risk.
Entraining a Robot to its Environment with an Artificial Circadian System · Georgia Tech · PhD
Lost to a baseline
Seasonal Naive forecasting beat ProphetNN in mean absolute error at the start and end of the year during low-supply periods (MAE 0.37M vs higher for Prophet models).
Post-Hoc, Contrastive, Explainable Artificial Intelligence for Time Series and Image Data · University College Dublin · PhD
Tried and failed
long short-term memory networks applied to long-horizon financial time series forecasting. Outcome: worse than baseline. Reason: obtained results that were below satisfactory performance levels
An AI Robot-Analyst System for Investment Analysis · Harvard · Master's
Tried and failed
LSTM neural networks for tabular time-series forecasting applied to macroeconomic and election time-series data. Outcome: did not converge. Reason: training frequently failed to converge error terms and accuracy without intensive manual architecture tuning
Exploring social and economic predictors for U.S. Government elections · Texas Tech · PhD
Sequential forecasting models suffer from error accumulation and trajectory instability
Multi-step sequential models experience severe performance degradation, sudden trajectory discontinuities, and phase misalignment over long prediction horizons. Inappropriate lookback lengths and unbuffered error propagation cause substantial predictive failures in downstream control and degradation tracking.
Tried and failed
short lookback windows in recurrent neural networks applied to time series demand forecasting. Outcome: worse than baseline. Reason: insufficient historical context captured to accurately model longer-term periodic demand patterns
Electricity Load Forecasting in Texas using Neural Networks to Enhance the Power Grid Stability · Texas Tech · Master's
Tried and failed
polynomial regression for time series forecasting applied to long-term cash flow forecasting. Outcome: did not generalise. Reason: models failed to maintain reasonable accuracy beyond a 12-month horizon
Sustainable Infrastructure Finance: Enhancing Transportation Construction Expenditure Management and Exploring Innovative Financing Mechanisms · Georgia Tech · PhD
Tried and failed
splitting states by task phases applied to macroscopic traffic flow forecasting. Outcome: worse than baseline. Reason: short-duration states were highly susceptible to measurement noise
Modeling and Optimization of Ridesplitting Operations · EPFL · PhD
Tried and failed
shortest-path search with single-state input applied to temporal duration forecasting. Outcome: worse than baseline. Reason: lacked velocity cues necessary to predict duration effectively
Context-aware motion prediction of human-object interaction · Imperial · PhD
Tried and failed
increasing input sequence length in recurrent networks applied to solar irradiance forecasting. Outcome: overfit. Reason: historical lag features added noise and increased validation error compared to single current measurements
Exploiting known structure in data-driven models of dynamical systems · UT Austin · PhD
Tried and failed
sequence-to-sequence LSTM for trajectory forecasting applied to long-term battery capacity degradation. Outcome: unstable. Reason: exhibited sudden initial prediction discontinuities and severe overestimation of degradation rates
Data-driven early prediction and modeling of lithium-ion battery capacity degradation · Iowa State · PhD
Tried and failed
multi-step sequential forecasting over long horizons applied to behavior representation learning. Outcome: unstable. Reason: training failed beyond short horizons whereas aggregate distribution prediction remained stable
Building a foundation model for neuroscience · Georgia Tech · PhD
Tried and failed
periodic time series forecasting for predictive control applied to robot environmental adaptation under phase shifts. Outcome: worse than baseline. Reason: phase-shifted disruptions caused out-of-sync forecasts, reducing performance to reactive baseline levels
Entraining a Robot to its Environment with an Artificial Circadian System · Georgia Tech · PhD
Tried and failed
model predictive control with lstm forecasting applied to solar-powered irrigation scheduling without battery buffer. Reason: solar forecasts had large positive-skewed errors, causing schedule misplacements and significant yield loss without energy storage
Product architectures for solar-powered drip irrigation (SPDI) systems in the Middle East and North Africa · MIT · PhD
Kalman filters and dynamic linear models suffer from over-smoothing and noise misspecification
Linear Kalman filtering with constant noise parameters either over-smooths dynamic signal spikes or fails to eliminate high-frequency sensor noise. In dynamic tracking tasks, misconfigured state space models remove informative variations and underperform simpler direct measurements or calibrated alternatives.
Tried and failed
Kalman filtering with signal quality indices applied to physiological time series denoising. Outcome: worse than baseline. Reason: Denoising removed motion and noise patterns that were informative and correlated with the target event
Self-Aware Machine Learning for Chronic Pathology Monitoring on Wearable Devices · EPFL · PhD
Tried and failed
linear Kalman filter applied to continuous glucose time series tracking. Reason: linear state transitions over-smoothed rapid non-linear spikes and drops
Novel Machine Learning Approaches with Applications in Healthcare and Social Welfare · Georgia Tech · PhD
Lost to a baseline
Dynamic linear model (SpDynLM) produced higher hold-out prediction error (RMSPE = 0.868) than Graphical Matern (RMSPE = 0.800) in the AR(1) spatial time-series simulation Set 3A
Topics in Modeling of Multivariate Mixed Data Types and Highly Multivariate Spatial Data · Johns Hopkins · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Evaluate wind time-series imputation models (e.g., LSTM, ARIMA) on data gaps exceeding 10% and during prolonged outages. Blocker: None
Data Driven Early Stage Design Support for Offshore Wind Farms · University College Dublin · PhD
Left open
Develop hybrid or ensemble forecasting models combining multiple RNN architectures with statistical models for multi-step wind power forecasting. Blocker: None
Deep Learning-Based Medium to Long-Term Multi-Step Ahead Wind Power Generation Forecasting · Texas State University · Master's
Left open
Develop and benchmark wind power forecasting models using the compiled open-source wind power dataset collection. Blocker: None
Multi-decadal wind power forecasting under climate change · Tübingen · PhD
Left open
Implement transformer architectures and model distillation techniques for multi-step ahead wind power generation forecasting. Blocker: None
Deep Learning-Based Medium to Long-Term Multi-Step Ahead Wind Power Generation Forecasting · Texas State University · Master's
Left open
Extend the energy management system framework to support wind power forecasting and integration using meteorological data fusion and regression models. Blocker: None
An improved energy management system framework for solar energy integration. · Cranfield · PhD
Left open
Model long-range dependency in wind power time series residuals using fractional Lévy stable motion (fLsm) with parameters alpha and H. Blocker: None
Graphon Mean Field Games with Finite States and Forecasting Models for the Energy Market · University of Trento · PhD
Left open
Develop a Bayesian deep learning model for wind power forecasting to capture uncertainty. Blocker: None
Towards intelligent operation of future power system: bayesian deep learning based uncertainty modelling technique · Imperial · PhD
Left open
Extend the univariate wind power forecasting models to multivariate architectures by incorporating meteorological and environmental exogenous variables. Blocker: None
Deep Learning-Based Medium to Long-Term Multi-Step Ahead Wind Power Generation Forecasting · Texas State University · Master's
Left open
Benchmark the adaptive linear regression drift-handling strategy against online ARIMA, SVM, Kalman filters, and RNNs on streaming time-series data. Blocker: None
Modelado Predictivo en Flujo de Datos de Procesos con Deriva de Concepto y su Aplicación al Turismo en Canarias · University of Las Palmas de Gran Canaria · PhD
Left open
Compare Generalized Network Autoregressive (GNAR) forecasting performance against Bayesian Vector Autoregression (BVAR) models on multivariate time series benchmarks. Blocker: None
New statistical and machine learning methods for time series forecasting · Imperial · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Tarik Ahasan Pranto · Texas State University · Master's · 2024
Deep Learning-Based Medium to Long-Term Multi-Step Ahead Wind Power Generation Forecasting - Rabin Dhakal · Texas Tech · PhD · 2023
Wind Speed Prediction using Classical Time Series and Machine Learning Models: A Comparative Analysis - Gohar Shoukat · University College Dublin · PhD · 2025
Data Driven Early Stage Design Support for Offshore Wind Farms - Wenqiu Ma · University of Washington · PhD · 2025
Bayesian Inference with Applications to Macroeconomics and Financial Market Price Discovery - Nina Effenberger · Tübingen · PhD · 2024
Multi-decadal wind power forecasting under climate change - Zhiyuan Wei · University at Buffalo · PhD · 2024
Towards Equitable Access to Essential Services: A Data-Driven Human Mobility Modeling Framework - Eoin Delaney · University College Dublin · PhD · 2024
Post-Hoc, Contrastive, Explainable Artificial Intelligence for Time Series and Image Data - Ziyi Dai · Georgia Tech · PhD · 2023
An Assessment of EV Adoption and Potential Growth under Evolving Techno-policy Scenarios - Matthew Joseph O'Brien · Georgia Tech · PhD · 2021
Entraining a Robot to its Environment with an Artificial Circadian System - Isuru Dassanayake · Texas Tech · PhD · 2021
Exploring social and economic predictors for U.S. Government elections - Chris Toumping Fotso · Leibniz Universität Hannover · PhD · 2026
Essays on Time-Varying Models in Time Series and Machine Learning - Abraham Isaac Camelo Guerrero · York University · Master's · 2026
A Novel Computational Ethology Framework for Studying Animal Behaviour Under Climate Change (CEFABC2): A Case Study of Little Penguins on Phillip Island
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