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arxiv: 2510.23090 · v2 · pith:5EAM7NMCnew · submitted 2025-10-27 · 💻 cs.CL

MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models

Suchan Lee , Jihoon Choi , Sohyeon Lee , Minseok Song , Bong-Gyu Jang , Hwanjo Yu , Soyeon Caren Han This is my paper

Pith reviewed 2026-05-22 12:30 UTC · model grok-4.3

classification 💻 cs.CL
keywords time-series forecastinglarge language modelsprompt engineeringmultimodal alignmentstatistical analysistemporal dependenciesforecasting framework
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The pith

MAP4TS adds global, local, statistical, and temporal prompts to raw time-series data to improve LLM forecasting.

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

The paper presents MAP4TS as a framework that augments numerical time-series inputs with four prompt types before feeding them to large language models. Global and local domain prompts supply dataset context and recent trends, while statistical and temporal prompts inject handcrafted features from autocorrelation, partial autocorrelation, and Fourier analysis. These elements are aligned cross-modality and processed by an LLM to generate forecasts. A sympathetic reader cares because existing LLM approaches for time series often ignore core statistical properties, and this structured prompting claims to deliver more accurate and stable results across diverse datasets without retraining the underlying model.

Core claim

By explicitly incorporating classical time-series analysis into prompt design through a Global Domain Prompt for dataset-level context, a Local Domain Prompt for recent trends, and Statistical and Temporal Prompts derived from ACF, PACF, and Fourier analysis, then combining them with raw time-series embeddings via a cross-modality alignment module, the LLM produces unified representations that yield more accurate forecasting than prior multimodal LLM methods.

What carries the argument

The Multi-Aspect Prompting Framework with its four specialized prompt components and cross-modality alignment module that unifies statistical insights with numerical embeddings for LLM input.

If this is right

  • Prompt-aware designs increase performance stability compared to raw numerical alignment alone.
  • GPT-2 backbones paired with these structured prompts can outperform larger models such as LLaMA on long-term forecasting tasks.
  • The approach consistently beats prior state-of-the-art LLM-based time-series methods across eight diverse datasets.

Where Pith is reading between the lines

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

  • Similar multi-aspect prompting could be tested on other sequential prediction tasks where domain statistics are available.
  • The results suggest that hybrid statistical-LLM systems might achieve strong performance with smaller backbone models rather than scaling model size.

Load-bearing premise

Handcrafted statistical insights from autocorrelation, partial autocorrelation, and Fourier analysis can be reliably translated into natural-language prompts that the LLM will use to capture temporal dependencies better than raw numerical alignment alone.

What would settle it

If experiments on new datasets show that removing the statistical and temporal prompts produces equal or better accuracy than the full MAP4TS setup, the benefit of those prompt components would be refuted.

Figures

Figures reproduced from arXiv: 2510.23090 by Bong-Gyu Jang, HwanJo Yu, Jihoon Choi, Minseok Song, Sohyeon Lee, Soyeon Caren Han, Suchan Lee.

Figure 1
Figure 1. Figure 1: Comparison of MAP4TS (red) and state-of-the-art [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overall architecture and procedure of MAP4TS and Examples of four-aspect prompts for the ETTh1 dataset: [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Attention map samples from ETTh1 and Traffic. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: The Climate dataset uses four-aspect prompts. The [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prediction results of MAP4TS and TimeLLM on [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Backbone LLM comparison on selected prompt com [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
read the original abstract

Recent advances have investigated the use of pretrained large language models (LLMs) for time-series forecasting by aligning numerical inputs with LLM embedding spaces. However, existing multimodal approaches often overlook the distinct statistical properties and temporal dependencies that are fundamental to time-series data. To bridge this gap, we propose MAP4TS, a novel Multi-Aspect Prompting Framework that explicitly incorporates classical time-series analysis into the prompt design. Our framework introduces four specialized prompt components: a Global Domain Prompt that conveys dataset-level context, a Local Domain Prompt that encodes recent trends and series-specific behaviors, and a pair of Statistical and Temporal Prompts that embed handcrafted insights derived from autocorrelation (ACF), partial autocorrelation (PACF), and Fourier analysis. Multi-Aspect Prompts are combined with raw time-series embeddings and passed through a cross-modality alignment module to produce unified representations, which are then processed by an LLM and projected for final forecasting. Extensive experiments across eight diverse datasets show that MAP4TS consistently outperforms state-of-the-art LLM-based methods. Our ablation studies further reveal that prompt-aware designs significantly enhance performance stability and that GPT-2 backbones, when paired with structured prompts, outperform larger models like LLaMA in long-term forecasting tasks.

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 / 2 minor

Summary. The manuscript proposes MAP4TS, a Multi-Aspect Prompting Framework for LLM-based time-series forecasting. It augments raw numerical embeddings with four prompt types—Global Domain Prompt (dataset-level context), Local Domain Prompt (recent trends), and Statistical/Temporal Prompts (handcrafted ACF, PACF, and Fourier insights)—then applies cross-modality alignment before LLM processing and projection to forecasts. The central claim is that this yields consistent outperformance over prior LLM-based methods across eight datasets, with ablations indicating that prompt-aware designs improve stability and that GPT-2 with structured prompts can surpass larger models like LLaMA on long-term tasks.

Significance. If the attribution to classical time-series analysis holds, the work offers a practical way to inject domain knowledge into LLM forecasting pipelines, potentially improving both accuracy and interpretability. The observation that smaller backbones benefit more from structured prompts is a useful practical insight. Significance is limited, however, by the absence of controls confirming that performance gains derive specifically from the semantic content of the ACF/PACF/Fourier prompts rather than from prompt length, format, or alignment mechanics alone.

major comments (2)
  1. [Ablation studies] Ablation studies section: The reported ablations remove Statistical/Temporal Prompts entirely but provide no control that replaces their specific ACF/PACF/Fourier-derived sentences with generic or length-matched neutral text. Without this isolation, it remains unclear whether the LLM actually attends to or benefits from the handcrafted statistical insights versus the multi-prompt scaffolding or cross-modality alignment module. This directly bears on the central claim that the framework incorporates classical time-series analysis to capture temporal dependencies.
  2. [§4.1] §4.1 Experimental Setup: Results across the eight datasets are presented without error bars, details on train/validation/test splits, random seeds, or statistical significance tests (e.g., Wilcoxon or paired t-tests). This makes it difficult to determine whether the reported outperformance is robust or could be explained by data partitioning choices.
minor comments (2)
  1. [Figure 1] The framework diagram would benefit from explicit arrows or labels showing how the four prompt components are concatenated before the cross-modality alignment step.
  2. [Method section] The description of how ACF/PACF values and Fourier coefficients are rendered into natural-language sentences could include one or two concrete prompt examples for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments on our manuscript. We have carefully considered each major comment and provide our responses below, indicating where revisions will be made to address the concerns.

read point-by-point responses

  1. Referee: [Ablation studies] Ablation studies section: The reported ablations remove Statistical/Temporal Prompts entirely but provide no control that replaces their specific ACF/PACF/Fourier-derived sentences with generic or length-matched neutral text. Without this isolation, it remains unclear whether the LLM actually attends to or benefits from the handcrafted statistical insights versus the multi-prompt scaffolding or cross-modality alignment module. This directly bears on the central claim that the framework incorporates classical time-series analysis to capture temporal dependencies.

    Authors: We agree that a control experiment using length-matched neutral text in place of the specific ACF/PACF/Fourier-derived prompts would help isolate the contribution of the semantic content from the prompts. Our current ablations demonstrate the overall benefit of including these prompts, but to strengthen the attribution to classical time-series analysis, we will add such a control in the revised manuscript. This will clarify whether the performance gains stem from the specific insights or the prompting structure itself. revision: yes

  2. Referee: [§4.1] §4.1 Experimental Setup: Results across the eight datasets are presented without error bars, details on train/validation/test splits, random seeds, or statistical significance tests (e.g., Wilcoxon or paired t-tests). This makes it difficult to determine whether the reported outperformance is robust or could be explained by data partitioning choices.

    Authors: We acknowledge the importance of reporting variability and statistical rigor in our experiments. In the revised manuscript, we will include error bars (e.g., standard deviation across multiple runs), provide explicit details on the train/validation/test splits for all datasets, specify the random seeds used, and conduct statistical significance tests such as paired t-tests or Wilcoxon signed-rank tests to validate the outperformance over baselines. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on external dataset experiments

full rationale

The paper proposes MAP4TS as a prompting framework that adds Global/Local Domain, Statistical, and Temporal prompts derived from ACF/PACF/Fourier analysis, then aligns them with time-series embeddings for LLM processing. All performance claims are grounded in experiments on eight external datasets plus ablations, with no equations, fitted parameters renamed as predictions, or self-citation chains that reduce the outperformance result to a quantity defined inside the method itself. The framework description contains no self-definitional steps or load-bearing uniqueness theorems imported from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on standard assumptions about LLM prompt effectiveness and alignment rather than new mathematical foundations or fitted constants.

axioms (1)
  • domain assumption Large language models can effectively integrate and utilize handcrafted statistical and temporal features when presented as natural language prompts alongside numerical embeddings.
    This premise underpins the cross-modality alignment module and the claim that the added prompts improve forecasting.

pith-pipeline@v0.9.0 · 5775 in / 1213 out tokens · 59707 ms · 2026-05-22T12:30:16.774616+00:00 · methodology

discussion (0)

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