arXiv 15 Jul 2026 · Econometrics
arXiv:2607.14279 · PDF · DOI · OpenAlex · Extracted main text
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: high dimensionality, nonstationarity, and 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Abdul Fatir Ansari and Lorenzo Stella and Ali Caner Turkmen and Xiyu… (2024) Chronos: Learning the Language of Time Series | 0.843 | 3 | 3 | 100% |
| 2 | Das, Abhimanyu and Kong, Weihao and Sen, Rajat and Zhou, Yichen (2024) A Decoder-Only Foundation Model for Time-Series Forecasting | 0.811 | 4 | 2 | 100% |
| 3 | Ho, Jonathan and Jain, Ajay and Abbeel, Pieter (2020) Denoising Diffusion Probabilistic Models | 0.811 | 4 | 2 | 100% |
| 4 | Lütkepohl, Helmut (2005) New Introduction to Multiple Time Series Analysis | 0.737 | 3 | 2 | 100% |
| 5 | Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Xiong, Caiming a… (2024) Unified Training of Universal Time Series Forecasting Transformers | 0.737 | 3 | 2 | 100% |
| 6 | Bai, Jushan and Ng, Serena (2002) Determining the Number of Factors in Approximate Factor Models | 0.644 | 2 | 2 | 100% |
| 7 | Engle, Robert F. and Granger, C. W. J (1987) Co-integration and Error Correction: Representation, Estimation, and Testing | 0.644 | 2 | 2 | 100% |
| 8 | Granger, C. W. J (1969) Investigating Causal Relations by Econometric Models and Cross-spectral Methods | 0.644 | 2 | 2 | 100% |
| 9 | Hamilton, James D (1994) Time Series Analysis | 0.644 | 2 | 2 | 100% |
| 10 | Johansen, Søren (1991) Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 71 scored citations.