Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment

arXiv:2508.07195 · cs.CL, cs.AI · Submitted 2026-08-16 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment".

Jane: The paper was written by Yanru Sun, Emadeldeen Eldele, Zongxia Xie, Yucheng Wang, Wenzhe Niu et al. from Tianjin University and Agency for Science, Technology and Research and Nanyang Technological University.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Title: Tom: Welcome back to the show, everyone! Today we are digging into a fresh arXiv paper, and the title alone is a mouthful: "Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment." Jane, what do you make of that title?

Jane: Tom, I love it because it tells you exactly what the problem is. We have these massive language models that are amazing with words, but time series data — like stock prices, weather readings, energy usage — is numbers over time. The title says we need to adapt LLMs to handle that, and the paper does it in two clever ways: modeling temporal heterogeneity and aligning semantics.

Tom: So, for our listeners who might not be deep in the weeds — time series forecasting is basically predicting the future from the past. Think of electricity demand tomorrow based on the last month of hourly readings. And LLMs, the things that power chatbots, they are really good at sequences of words. So the question is, can we get them to be good at sequences of numbers?

Jane: Exactly. And the authors — Yanru Sun, Emadeldeen Eldele, Zongxia Xie, and a whole team from Tianjin University and A*STAR in Singapore — they noticed something important. Time series data is messy. It's not one uniform pattern. You might have a smooth trend for a while, then a sudden spike, then a lot of noisy fluctuation. That's the "heterogeneity" part. Language, on the other hand, has pretty consistent grammar and structure.

Tom: Right, so a language model trained on text might get confused when you feed it numbers that jump around unpredictably. And there's another gap — the "modality gap." Words are discrete symbols. Numbers are continuous. They live in different worlds.

Jane: And that's what makes this paper exciting. They're not just slapping an LLM onto time series data and hoping for the best. They're building a bridge. They call their framework TALON, which sounds like a bird of prey, but it stands for Temporal-heterogeneity And Language-Oriented Network.

Tom: A bird of prey for forecasting — I like that image. And the key idea is that instead of treating all time segments the same, they split them up and route each segment to a specialized expert. Some segments get a linear model, some get a CNN, some get an LSTM. It's like having a team of specialists instead of one generalist.

Jane: And then they align the numerical features with language-like embeddings, so the LLM can actually reason about the numbers in a way that makes sense. We'll get into the weeds in a minute, but the headline is that they report up to eleven percent improvement in mean squared error over state-of-the-art methods across seven benchmarks.

Tom: Eleven percent is huge in this field. And they do it without needing handcrafted prompts during inference, which is a big deal for real-world deployment. We'll talk about how they pull that off next — stay with us.

Summary: Tom: So we're back with "Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment." Jane, let's break down the actual architecture because I think that's where the magic is.

Jane: Sure. The paper has three main components. First, there's the Heterogeneous Temporal Encoder, or HTE. Imagine you have a long sequence of numbers — say, six hundred seventy-two time steps. They chop it into small patches, like little windows of data. For each patch, they compute some simple statistics: trend strength, local variation, and autocorrelation. That tells them whether the patch is smooth, jumpy, or repetitive.

Tom: And based on those statistics, they route the patch to a specialist. Like, if it's a smooth trend, send it to the linear expert. If it's got local spikes, send it to the CNN expert. If it's got long-range memory, send it to the LSTM expert. That's the "heterogeneous" part — different tools for different patterns.

Jane: Exactly. And they use a gating mechanism that's inspired by mixture-of-experts models. It's a bit like a traffic controller that looks at each patch and decides which lane it belongs in. They even add a regularization term to make sure all experts get used — otherwise the model might just default to one expert and lose the benefit of specialization.

Tom: That's smart. Now, the second component is the Semantic Alignment Module, or SAM. This is where the modality gap gets addressed. The idea is to generate a little text prompt for each patch — not a generic prompt, but one that describes that specific patch's characteristics. Like "this patch has high trend strength and low variation." Then they embed that text using the LLM's tokenizer.

Jane: And then they use contrastive learning to pull the numerical features and the text embeddings closer together in a shared space. So the numbers and the words start to mean the same thing to the model. That's the "semantic alignment" in the title.

Tom: And the third piece is the LLM Forecasting Head. They take those aligned features, feed them into a frozen pretrained LLM — they use GPT-two in the experiments — and then a lightweight decoder produces the forecast. The LLM does the heavy lifting of reasoning over the sequence.

Jane: One thing I really appreciate is that they don't need prompts during inference. The prompts are only used during training to teach the model the alignment. At test time, you just feed the raw time series, and the model knows what to do. That makes it fast and practical.

Tom: And the results back it up. They tested on seven datasets — ETT, Weather, Electricity, Traffic — and they beat both LLM-based and deep learning baselines. In the one-for-all setting, where you train one model and use it for all prediction horizons, they got the lowest error on every single dataset.

Jane: And in zero-shot forecasting, where you train on one dataset and test on a completely different one, they won eleven out of twelve tasks. That's a strong sign that the model is learning general patterns, not just memorizing a dataset.

Tom: So the architecture is clever, but the real question is — how does this change the game in practice? We'll get into the improvements and implications next.

Improvements: Tom: We're back with "Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment." Jane, we've covered the architecture — now let's talk about what this actually improves over existing methods, because there are a lot of LLM-based forecasting papers out there.

Jane: Right. The paper is very clear about what's wrong with the current approaches. There are two main camps. One camp tokenizes the time series — basically converts the numbers into discrete symbols, like turning a waveform into a series of letters. The problem is that discretization loses the fine-grained numerical structure. You're throwing away precision.

Tom: And the other camp uses prompts — they prepend a handcrafted text template like "predict the next value" to the numerical input. The problem there is that the prompt is static and generic. It doesn't adapt to what the data actually looks like. And you have to design those prompts manually, which is a pain.

Jane: TALON improves on both. Instead of discretizing, it keeps the continuous values but aligns them with language embeddings. And instead of a static prompt, it generates a token-adaptive prompt that describes each patch's specific characteristics. So the alignment is fine-grained, not one-size-fits-all.

Tom: And that's a real improvement. The paper shows that if you remove the semantic alignment module, the MSE goes up by about seven percent. If you replace the token-adaptive prompt with a static one, it goes up by about five point six percent. So every piece is pulling its weight.

Jane: And the heterogeneous encoder is also validated. They did an ablation where they removed each expert type one at a time. Removing the linear expert hurt by eight point one percent, removing CNN by seven point nine percent, removing LSTM by five point eight percent. So all three specialists are contributing, and together they're better than any single one.

Tom: Another improvement is efficiency. A lot of LLM-based forecasters are heavy — they need big models and long inference times. TALON keeps the LLM frozen and only trains a small encoder and decoder. They report about one point seven million trainable parameters and inference around two seconds on a standard benchmark. That's competitive with much lighter models.

Jane: And they also show generality across different LLMs. They swapped GPT-two for Qwen, Deepseek, and LLaMA, and TALON still beat the strong baseline AutoTimes on every dataset. So it's not tied to one specific language model — it's a framework that works with whatever LLM you have.

Tom: That's a big deal for adoption. If you already have a deployed LLM, you can slot TALON in without changing your whole stack.

Jane: And the zero-shot results we mentioned earlier — that's the real proof of generalization. Training on ETTh1 and testing on ETTh2, or training on ETTm1 and testing on ETTm2 — those are different domains with different resolutions. TALON consistently wins. That suggests it's learning something about temporal structure itself, not just memorizing a dataset.

Tom: So the improvements are clear: better accuracy, better generalization, less manual engineering, and better efficiency. Now, what does this mean for the world beyond the benchmark? Let's bring in Lu and Meng for that.

Conclusion: Tom: Alright, we're wrapping up our discussion of "Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment." Jane, let's bring in Lu and Meng to get their take on the bigger picture.

Jane: Absolutely. Lu, you're the researcher — what excites you most about this paper?

Lu: I'm excited about the semantic alignment idea. We're not just using an LLM as a fancy sequence model. We're teaching it to understand the *meaning* of a time series patch — that it's trending up, that it's noisy, that it has long-range dependencies. That opens the door to combining time series with actual text. Imagine a system that reads a weather report and a temperature sensor and makes a forecast using both. That's a genuinely new capability.

Meng: And from an engineering standpoint, I love that it's prompt-free at inference. That means no prompt engineering, no runtime tokenization of text, no extra latency. You train once, and then it's just numbers in, numbers out. That's the kind of thing that makes a model actually deployable in production.

Jane: And the efficiency numbers back that up — one point seven million trainable parameters and two-second inference. That's not a research toy; that's a real system.

Tom: And the zero-shot results are the cherry on top. Training on one domain and forecasting on another without any fine-tuning — that's the dream for a lot of applications, from energy grids to supply chains.

Lu: Exactly. And the paper is honest about its limitations — it uses GPT-two as the backbone, which is small by today's standards. But they show it works with bigger models too. So the framework is future-proof.

Meng: The only thing I'd want to see more of is stress-testing on even more diverse datasets — maybe financial data or medical signals. But the seven benchmarks they used are already pretty broad.

Jane: I think the biggest takeaway is that this paper shows a path forward. We don't have to choose between the power of LLMs and the precision of time series models. We can have both, if we're smart about how we bridge the gap.

Tom: And that's the note we'll end on. "Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment" — a framework that treats time series with the respect it deserves, and lets LLMs do what they're good at. Thanks to Lu, Meng, and of course Jane, for a great discussion.

Jane: Thanks, everyone. Next up, we've got a paper on vision-language models that I think is going to spark some debate. See you then!

Tom: Take care, folks.

Yanru Sun, Emadeldeen Eldele, Zongxia Xie, Yucheng Wang, Wenzhe Niu, Qinghua Hu, Chee Keong Kwoh, Min Wu

Tianjin University · Agency for Science, Technology and Research · Nanyang Technological University

cs.CL, cs.AI

Submitted: 2026-08-16

Updated: 2026-08-18

Code: https://github.com/syrGitHub/TALON

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 63/100

Key concepts

Time Series Forecasting
This is the process of predicting future values based on historical data points over time. For example, estimating tomorrow's electricity demand using hourly readings from the past month is a form of time series forecasting.
Temporal Heterogeneity Modeling
This concept recognizes that time series data is not uniform; it contains various patterns like smooth trends or sudden spikes. The model addresses this by splitting the data into patches and routing each segment to a specialized expert, such as a linear or CNN model.
Semantic Alignment Module (SAM)
This module bridges the gap between numbers and language. It generates text prompts that describe specific characteristics of data segments, then uses contrastive learning to align these textual meanings with the actual numerical features for use by an LLM.
TALON Framework
The complete architecture designed to adapt LLMs for time series prediction. It combines a heterogeneous encoder—which routes data to specialized experts—with semantic alignment, enabling the frozen LLM to reason over continuous numerical sequences.

Terminology

Summary

Summary

This paper introduces TALON (Temporal-heterogeneity And Language-Oriented Network), a unified framework for LLM-based time series forecasting that addresses two fundamental issues: the inherent heterogeneity of temporal patterns and the modality gap between continuous numerical signals and discrete language representations.

The paper identifies that multivariate time series often exhibit intrinsic heterogeneity, where different segments and variables follow diverse and evolving patterns, whereas LLMs are pretrained on text corpora with globally consistent grammatical structures. Additionally, time series are continuous and real-valued, governed by strong temporal dependencies, whereas LLMs are inherently designed for discrete, symbolic sequences. Existing LLM-based forecasting methods fall into two categories: Tokenization-based methods, which discretize continuous sequences into symbolic tokens and Prompt-conditioned methods, which prepend handcrafted textual templates to time series inputs. Both paradigms fail to fully account for the modality gap as they either disrupt temporal continuity, discard fine-grained numerical structure, or suffer from weak alignment and a reliance on manually constructed prompts.

TALON consists of three key components: the Heterogeneous Temporal Encoder (HTE), the Semantic Alignment Module (SAM), and the LLM Forecasting Head (LFH). The framework follows the channel-independent strategy, decomposing the multivariate input into C separate univariate sequences, each segmented into N consecutive non-overlapping patches of length S.

The Heterogeneous Temporal Encoder (HTE) learns pattern-aware representations by dynamically adapting expert selection to the complexity and temporal structure of each input patch. It consists of three components: Pattern Quantification, Pattern-Adaptive Routing, and Heterogeneous Pattern Extractors. Pattern Quantification computes a compact set of interpretable token-level statistical features (e.g., trend strength, variation, and autocorrelation) for each patch, forming a quantification vector ci = [c1, c2, c3] ∈ R3. Pattern-Adaptive Routing introduces latent uncertainty into the expert selection process by encoding both the input patch and its complexity into latent scores, injecting Gaussian noise, and using a top-k selection before softmax to promote sparsity. The Heterogeneous Pattern Extractors consist of a lightweight expert pool comprising three branches, each endowed with distinct inductive biases: a Linear Expert for modeling trend-like patterns, a CNN Expert for capturing local dependencies, and an LSTM Expert for modeling long-term memory. The final representation is a weighted aggregation over all expert outputs. An Expert Regularization term, LMoE = Limportance + Lload, is incorporated to prevent expert collapse and promote diverse expert usage.

The Semantic Alignment Module (SAM) performs fine-grained token-level alignment between temporal features and their corresponding textual semantics via contrastive learning. It generates Token-Adaptive Prompts that integrate three aspects: (1) expert routing hints, (2) patch-wise temporal context, and (3) complexity-aware features. The prompt template includes sections for [Expert Routing Hint], [Patch Time Context], and [Complexity Features] with placeholders for trend strength, local variation, and temporal dependency. These prompts are tokenized using the LLM tokenizer to yield prompt embeddings pi. The alignment is achieved through a contrastive objective: Lalign = - (1/N) Σ log [exp(⟨ei, pi⟩ / τ) / Σ exp(⟨ei, pi⟩ / τ)], where ⟨·,·⟩ denotes cosine similarity, τ is a temperature parameter, and all vectors are l2-normalized.

The LLM Forecasting Head (LFH) passes the aligned features ei through a frozen pretrained LLM to perform deep contextual reasoning: f = LLM(e), followed by a lightweight decoder: Ŷ = MLP(f). The autoregressive decoding allows flexible forecasting without retraining for different horizons. The final training objective is L = LMSE + αLMoE + βLalign.

The inference pipeline is streamlined and fully prompt-free, with input time series segmented into patches, each of which is dynamically routed to the most suitable expert within the Heterogeneous Temporal Encoder, producing semantically enriched features passed through the frozen LLM for autoregressive forecasting.

Experiments are conducted on seven benchmarks: ETTh1, ETTh2, ETTm1, ETTm2, Weather, Electricity, and Traffic. The input lookback window is fixed to L = 672, using pre-trained GPT2 based model with the first 6 Transformer layers as our backbone. Two evaluation protocols are used: a one-for-all rolling forecast setting where a single model is trained on a 96-step prediction horizon and then directly applied to all other horizons, and a conventional one-for-one setting where a separate model is trained and evaluated for each prediction horizon.

In the one-for-all setting, TALON consistently achieves the lowest MSE across all seven datasets, with an average improvement of up to 10% over state-of-the-art deep forecasters and 12% over recent LLM-based methods. In the one-for-one setting, it further achieves state-of-the-art performance with up to 20% MSE reduction. In zero-shot forecasting, TALON consistently achieves the best MSE performance in 11 out of 12 tasks, with 7%∼19% relative MSE improvement. Compared with MoE-based methods, TALON achieves the best MSE scores across all datasets, with an average improvement of 7% to 16% over existing methods.

Ablation studies show that removing the full HTE module (w/o HTE) increases average MSE by 8.6%, while disabling only the routing mechanism (w/o HTE R) leads to a 8.1% increase. Disabling SAM results in a 7.2% increase in MSE, and replacing the token-adaptive prompt with a static TimeLLM-style prompt leads to 5.6% degradation. Removing the LLM causes the most significant drop, with a 33.9% increase in MSE. Analysis of HTE shows that removing any single expert type leads to notable performance degradation (8.1%, 7.9%, and 5.8%, respectively). Expert assignment analysis reveals that the expert utilization patterns vary significantly across datasets, with Weather showing a strong preference for Expert 0, whereas ETTh2 and ETTm1 exhibit more balanced and diverse assignments.

Generality evaluation shows TALON is designed to be compatible with any decoder-only LLM, consistently outperforming AutoTimes across GPT-2, Qwen, Deepseek, and LLaMA backbones. Efficiency analysis shows TALON achieves the lowest MSE while maintaining a compact model size (∼ 1.7M) and fast inference (∼ 2s). Parameter sensitivity analysis demonstrates performance remains relatively stable across a wide range of α and β values, and sensitivity analysis on top-k shows k = 2 yields the most first-place results overall (9 for both MSE and MAE), while k = 3 also performs strongly.

The paper concludes that TALON enables off-the-shelf large language models to perform pattern-aware and semantically aligned forecasting across diverse scenarios, achieving state-of-the-art accuracy while maintaining high efficiency and scalability.

Improvements for AI systems

Based on the TALON paper, here are the specific improvements I can implement in AI systems, along with the resulting capabilities:

  1. Heterogeneous Temporal Encoder (HTE) with Pattern-Adaptive Routing

  • Implementation: Replace uniform patch encoders with a mixture of three specialized experts (Linear, CNN, LSTM) selected via a stochastic gating mechanism. Each patch is quantified by trend strength, local variation, and autocorrelation, then routed to the top-k experts.

  • Resulting Capability: The system can dynamically adapt to non-stationary and diverse temporal patterns (e.g., trends vs. fluctuations vs. long-term dependencies) without manual feature engineering or dataset-specific tuning.

  1. Semantic Alignment Module (SAM) with Token-Adaptive Prompts

  • Implementation: Generate per-patch textual prompts encoding expert routing hints, temporal context (start/end times, sequence length), and complexity features. Use contrastive learning (InfoNCE-style loss) to align time-series embeddings with these prompt embeddings in a shared latent space.

  • Resulting Capability: The system bridges the modality gap between continuous signals and discrete language, enabling the LLM to interpret time series with semantic grounding—without requiring handcrafted prompts during inference.

  1. Prompt-Free Autoregressive Forecasting Head (LFH)

  • Implementation: After alignment, feed the semantically enriched features into a frozen pretrained LLM (e.g., GPT-2) followed by a lightweight MLP decoder. Use autoregressive decoding to generate future segments of arbitrary length.

  • Resulting Capability: The system can perform variable-horizon forecasting (e.g., 96, 192, 336, 720 steps) with a single trained model, eliminating the need for retraining per horizon and reducing inference overhead.

  1. Expert Regularization for Load Balancing

  • Implementation: Add importance and load-balancing losses to prevent expert collapse, ensuring all three experts are utilized across diverse temporal patterns.

  • Resulting Capability: The system maintains stable training and avoids degenerate solutions where one expert dominates, improving generalization across datasets with varying complexity.

  • Achieve State-of-the-Art Forecasting Accuracy: On seven real-world benchmarks (ETTh1/2, ETTm1/2, Weather, Electricity, Traffic), the system reduces MSE by up to 11% over recent LLM-based methods and up to 20% over deep learning baselines (e.g., PatchTST, iTransformer, TimesNet).

  • Generalize Zero-Shot Across Domains: The system transfers across different time resolutions (hourly→minute) and domains (e.g., ETTh1→ETTh2) with 7–19% MSE improvement over baselines, without any target-domain training.

  • Operate Prompt-Free at Inference: Unlike TimeLLM or CALF, the system does not require textual prompts during inference, reducing input complexity and computational cost while preserving temporal continuity and numerical precision.

  • Adapt to Any Decoder-Only LLM: The framework is model-agnostic—replacing GPT-2 with Qwen, Deepseek, or LLaMA consistently improves forecasting over AutoTimes, confirming robust transferability.

  • Handle Heterogeneous Temporal Patterns: The system automatically routes segments to specialized experts (Linear for trends, CNN for local fluctuations, LSTM for long-term memory), enabling accurate forecasting under non-stationarity and regime shifts.

  • Maintain Efficiency: With 1.7M trainable parameters and 2s inference time on ETTh1-96, the system outperforms larger models (e.g., Timer XL) while being deployable on resource-constrained hardware.

  • Provide Interpretable Routing: The expert assignment distributions reveal dataset-specific pattern complexity (e.g., Weather prefers Linear, ETT datasets use balanced routing), offering insights into temporal dynamics for debugging and domain analysis.

These improvements enable a forecasting system that is accurate, efficient, and robust across diverse real-world applications (e.g., energy load, traffic, weather), while being compatible with existing LLM infrastructure.

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