Transformer Architectures for Time Series Forecasting: Recent Advances in E

Speaker: Prof. Dr. Jihoon Moon

Affiliation: Department of Data Science, Duksung Women's University, Seoul, South Korea

Chair: Yang-Sun Lee

Time series forecasting constitutes a foundational capability across diverse industrial sectors, from energy grid management and financial market prediction to supply chain optimization and climate modeling. The emergence of transformer-based architectures over the past two years has fundamentally transformed the landscape of time series analysis, delivering unprecedented accuracy in multi-horizon prediction tasks while providing novel capabilities for interpretability and scalability. This presentation surveys recent advances (2024-2025) in transformer architectures specifically designed for time series forecasting, with particular emphasis on energy systems and industrial applications where accurate predictions directly impact operational efficiency and sustainability objectives.

Traditional approaches to time series forecasting, including autoregressive integrated moving average (ARIMA), exponential smoothing, and classical machine learning ensembles, have demonstrated considerable success in capturing linear dependencies and stationary patterns. However, these methods encounter fundamental limitations when confronting the complex, nonlinear, multi-scale temporal dynamics characteristic of modern industrial systems. Recurrent neural networks (RNNs) and their variants—long short-term memory (LSTM) and gated recurrent units (GRU)—addressed some limitations by learning sequential dependencies, yet suffer from vanishing gradients, limited parallelization, and difficulty capturing long-range dependencies spanning hundreds or thousands of time steps.

Transformers, originally developed for natural language processing, overcome these architectural constraints through self-attention mechanisms that model relationships between all time points simultaneously, enabling efficient parallel computation and explicit modeling of long-range dependencies. Recent research has developed specialized transformer variants optimized for time series characteristics. The Informer architecture introduces ProbSparse self-attention to reduce computational complexity from quadratic to logarithmic scaling, enabling application to sequences containing tens of thousands of time points. iTransformer fundamentally reconceptualizes the transformer paradigm by treating individual time series variates as tokens rather than time points, achieving superior performance in multivariate forecasting scenarios. PatchTST segments time series into semantic patches analogous to image patches in vision transformers, capturing local temporal patterns while maintaining global context through attention mechanisms.

This presentation examines comparative evaluations across fourteen state-of-the-art forecasting models applied to energy systems, including photovoltaic power generation, wind energy production, electrical load forecasting, and district heating demand prediction. Empirical results demonstrate that transformer-based architectures—particularly Informer, iTransformer, PatchTST, and FEDformer—achieve superior accuracy across multiple evaluation metrics compared to both classical statistical methods and RNN-based deep learning approaches. In photovoltaic power forecasting, transformer models achieve median absolute errors below 1.25 MW across diverse weather conditions, compared to 2.1 MW for LSTM baselines. Long-term electrical load forecasting exhibits root mean squared percentage errors of 0.66% for transformer architectures versus 1.8% for traditional ensemble methods.

The SolarNexus framework exemplifies the practical deployment of transformer-based forecasting in renewable energy management. This adaptive system combines Temporal Convolutional Networks (TCN) with Multi-Head Attention (MHA) mechanisms to capture both short-term fluctuations driven by weather dynamics and long-term seasonal patterns in solar irradiance. Online learning capabilities enable continuous model updates as new observations arrive, maintaining accuracy under evolving climate conditions without requiring complete retraining. Transfer learning techniques facilitate cross-regional deployment—models pretrained on data from established solar installations can be fine-tuned for new locations with minimal historical data, dramatically accelerating deployment timelines and reducing data collection costs. Operational deployment across multiple photovoltaic facilities demonstrates forecast accuracy improvements of twenty-three percent compared to previous state-of-the-art methods, translating to more efficient grid integration and reduced curtailment losses.

Industrial applications beyond energy systems reveal the broad applicability of transformer-based forecasting. In supply chain management, multi-step ahead demand forecasting incorporating exogenous variables (promotions, holidays, economic indicators) enables more effective inventory optimization and procurement planning. Financial market applications leverage attention mechanisms to identify relevant features from high-dimensional datasets encompassing market indices, commodity prices, sentiment indicators, and macroeconomic variables. Manufacturing process control benefits from accurate prediction of equipment performance metrics, enabling predictive maintenance strategies that minimize downtime while avoiding unnecessary interventions.

Explainability represents a critical advantage of attention-based transformer architectures in industrial applications where stakeholders require understanding of model reasoning to validate predictions and guide decision-making. Attention weight visualization reveals which historical time periods and which variables most strongly influence specific forecasts, providing actionable insights beyond raw predictions. SHAP (SHapley Additive exPlanations) analysis applied to transformer-based forecasting models quantifies the marginal contribution of individual features to prediction outcomes, enabling domain experts to validate model behavior against physical understanding and identify potential model failures or data quality issues. In energy forecasting contexts, explainability analysis has revealed that transformer models appropriately weight recent weather conditions and historical demand patterns while identifying unexpected dependencies between seemingly unrelated variables that warrant further investigation.

The presentation addresses practical implementation considerations critical for successful deployment in industrial settings. Hyperparameter optimization strategies balance forecast accuracy, computational efficiency, and generalization performance across diverse operating regimes. Multi-step forecasting approaches—recursive prediction versus direct multi-output modeling—exhibit different trade-offs between short-term precision and long-term stability. Uncertainty quantification methods, including conformal prediction and quantile regression, provide probabilistic forecasts that support risk-aware decision-making and enable more sophisticated optimization under uncertainty. Computational resource requirements vary substantially across architectures; understanding these trade-offs enables appropriate model selection given deployment constraints spanning edge devices, cloud infrastructure, and hybrid architectures.

Emerging research directions promise further advances in transformer-based time series forecasting. Multi-modal learning frameworks integrate diverse data sources—numerical time series, textual information from maintenance logs, image data from monitoring systems—to enhance prediction accuracy and provide richer contextual understanding. Hierarchical transformers model time series at multiple temporal resolutions simultaneously, capturing both fine-grained short-term dynamics and coarse-grained long-term trends. Causal transformers move beyond purely correlational modeling to identify causal relationships between variables, enabling more reliable counterfactual reasoning and what-if analysis for scenario planning. Few-shot learning techniques aim to enable accurate forecasting with minimal historical data, addressing cold-start problems when deploying systems in new contexts or for newly installed equipment.

Benchmarking methodologies and standardized evaluation frameworks facilitate rigorous comparison across competing approaches and accelerate research progress. The NeuralForecast library provides unified interfaces to state-of-the-art models including transformers, enabling streamlined experimentation and deployment. Open datasets spanning diverse domains—electricity markets, solar and wind generation, building energy consumption, transportation demand—enable reproducible research and fair algorithmic comparison. Evaluation metrics beyond point forecast accuracy, including prediction interval coverage, calibration error, and decision-oriented metrics quantifying business value, provide more comprehensive assessment of practical utility.

This survey synthesizes recent theoretical advances and empirical findings in transformer-based time series forecasting, providing researchers with comprehensive understanding of state-of-the-art techniques and practitioners with actionable guidance for deploying these powerful architectures in energy systems and industrial applications. The dramatic accuracy improvements, enhanced interpretability, and flexible deployment options offered by transformer models position them as the methodology of choice for next-generation forecasting systems supporting data-driven decision-making in an increasingly complex and dynamic operational landscape.