It is argued that modern methods make progress by expanding the classical forecasting template: they allow more flexible dynamics, use larger information sets and training corpora, and represent richer predictive distributions, yet they often lack the inferential and structural tools that make classical models useful for testing, explanation, and policy analysis.
Abstract
Forecasting is a central goal of time-series analysis. This review centers on three major developments in recent AI-based time-series forecasting: transformers, large pretrained models for zero-shot forecasting, and diffusion-based generative forecasters. We connect these methods to the econometric tradition built around the vector autoregression (VAR) through a common object: the conditional distribution of the future given the past. The review is organized around three long-standing challenges: \emph{high dimensionality}, \emph{nonstationarity}, and \emph{nonlinearity}. We argue that modern methods make progress by expanding the classical forecasting template: they allow more flexible dynamics, use larger information sets and training corpora, and represent richer predictive distributions. Yet they often lack the inferential and structural tools that make classical models useful for testing, explanation, and policy analysis. We close by outlining open problems where econometric tools remain important.
The literature is organized around five recurring difficulties: nonlinear and nonstationary behavior, contamination and structural breaks, uncertainty, long contexts and cross-variable dependence, and limited target-domain data.
Chu-Ting Wen· Applied and Computational En...· 0 citations
This work proposes CvLoss, a plug-in structural regularizer that constrains forecast residuals on a cross-variable graph and shows that CvLoss consistently improves competitive forecasting models, outperforms representative learning objectives, and is compatible with a variety of forecasting backbones.
Kuiye Ding, Yifan Hu, Hanchen Wang et al.· 0 citations
This study evaluates multiple forecasting models, ranging from HAR and GARCH to Tree-based and Neural architectures, across 14 Global Equity Indices and Horizons form 1 day to 100 Trading days within a strictly chronological and capacity-controlled framework, indicating that for strongly dependent time series, nominal sample size is a misleading measure of learnability, and model capacity must be constrained relative to effective information rather than observation count.
The findings demonstrate that rigorous leakage-free validation is essential for reliable forecasting research and that, for monthly Robusta coffee prices, increased model complexity does not necessarily yield superior predictive performance.
Dler H Kadir, D. Khalil, Azhin M. Khudhur· Forecasting· 0 citations
Multivariate time series forecasting that uses past observations to predict future data has been widely used in various application. Numerous methods have been developed, including deep learning based approaches. One of the most successful models is Mamba, which is based the state space models (SSMs). However, Mamba and its variants still face the challenges of long training times owing to its nonlinear operations. In this paper, we proposed a Fast Exponential Decay Approach (FEDA) to improve the training efficiency of Mamba. We have observed that the hidden states in the model are linear combinations of past inputs. Therefore, we attempted to use a fully connected layer to generate all the hidden states. Although a fully connected layer does not have a recurrent structure, based on the intuitive idea in multivariate time series forecasting that the influence of observations diminishes over time, we multiplied the weights of the fully connected layer by a time-dependent coefficient to emulate a recurrence-like behavior. We evaluated our model on various datasets, and the results show that our model achieves the performance comparable to the state of the art models, while significantly improving their training efficiency. More specifically, it reduces training time by 20% to 80% depending on the channels of the dataset.
We develop fast methods for conditional forecasting and structural scenario analysis with high-dimensional Bayesian vector autoregressions (VARs). Our general framework features a factor structure on the reduced-form errors, which enables fast and order-invariant equation-by-equation estimation; suitably identified factors admit a structural interpretation. The scenarios are defined through separate distributional restrictions on observables, structural shocks and idiosyncratic components. The computational cost of our proposed algorithm is cubic only in the number of restrictions, while the dimension of the forecasted system enters linearly. In our application with $33$ macroeconomic and financial variables and ten set-identified structural shocks for the US, we compute counterfactual predictions for oil price scenarios in the context of the 2026 closure of the Strait of Hormuz. The same oil price path is consistent with outcomes ranging from a mostly benign episode to pronounced stagflation, depending on which structural and idiosyncratic shocks are allowed to deliver it.
Niko Hauzenberger, Michael Pfarrhofer· 0 citations
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