TimesJev: Thinking Fast on Time Series with System 1 Sufficiency

Chenghao Liu; Wei Fan; Jindong Han; Dongjie Wang; Aniket Mahanti; Pengyang Wang; Hao Liu

cs.AIOct 19, 2026

TimesJev: Thinking Fast on Time Series with System 1 Sufficiency

Authors: Chenghao Liu, Wei Fan, Jindong Han, Dongjie Wang, Aniket Mahanti, Pengyang Wang, Hao Liu

Organizations: University of Auckland · Shandong University · Northeast Normal University · University of Macau ·

Abstract

Large language models (LLMs) have recently shown strong potential for time series reasoning, while models that reason over time series are usually with long chains of thought. However, this process often consumes thousands of tokens, directly leading to substantial latency during inference. In contrast, System 1 models such as Jev make decisions instantly and can naturally be an alternative, but raw time series are not compatible with the embedding space of these models, thus hindering the reasoning performance, especially when input series contain thousands of numeric points. In this paper, we make a very early attempt to propose a time series System 1 model, namely TimesJev. Specifically, we first formalize System 1 sufficiency in time series reasoning, which motivates a compressed representation for time series with key frequency patterns preserved. To construct this representation, we derive a dimension reduction objective that combines (i) a sufficiency divergence that measures the core information lost during compression, and (ii) a realizability divergence that quantifies the incompatibility of the System 1 model to the optimal prediction given this representation. We optimize this objective by learning time series compression with a gating mechanism for sufficiency, and then aligning the selected representation with the embedding space for realizability. Finally, experiments on 13 datasets demonstrate that TimesJev outperforms reasoning models by over 62% with 62.9% less inference time than their fine-tuned versions on average.

Figures & tables

Figure 1: Accuracy versus inference latency on time series tasks, with bubble size denoting generated tokens.

Figure 2: Overview of TimesJev. (a) Inference with a System 1 model in one forward pass. (b) Compressing the time series through stochastic gates to reduce the sufficiency divergence. (c) Aligning the compact representation with the model through learned embeddings to reduce the realizability divergence.

General-Purpose LLMs
Method		Anomaly Detection				Classification	MCQA				True/False QA				Overall
Method		Environment	AIOps	Healthcare	Web	Human Activity	Healthcare	Nature	Web	Other	Healthcare	Nature	Web	Other	Overall
Qwen3.5-9B	Zero-shot	49.60	63.00	44.80	58.20	22.80	61.96	77.50	83.61	55.81	71.68	74.59	80.95	79.31	54.31
Qwen3.5-9B	LoRA SFT	79.20	98.00	99.20	91.80	76.80	71.74	86.67	86.89	72.09	84.27	88.52	87.30	89.66	86.91
Qwen3-8B	Zero-shot	50.00	50.40	54.00	59.80	21.20	63.04	66.67	90.16	53.49	70.98	80.33	88.89	72.41	53.91
Qwen3-8B	LoRA SFT	69.20	97.20	99.00	85.00	78.40	68.84	78.33	86.89	67.44	77.27	88.52	88.89	89.66	83.51

Table 1: Main results on TSQA in accuracy (%). LoRA SFT denotes LoRA fine-tuning on the TSQA training set. The best result is in bold and the second best is underlined . † Official checkpoints fine-tuned on TSQA.

Method		Tokens	TFLOPs	Latency (ms)	Overall ( $\uparrow$ )
General-Purpose LLMs
Qwen3.5-9B	Zero-shot	1535	35.97	12528	54.31
Qwen3.5-9B	LoRA SFT	7	6.20	102	86.91
Qwen3-8B	Zero-shot	577	14.79	4835	53.91
Qwen3-8B	LoRA SFT	7	5.22	103	83.51
Time Series Reasoning Models

Table 2: Test-time cost and accuracy on TSQA.

\approx

0 indicates that the answer is read from option probabilities without decoding. Overall is the accuracy (%) in Table 1 . † Models fine-tuned on TSQA.

Figure 3: Accuracy by domain when the feature values are kept, the most important one is permuted, or all are shuffled across questions.

Figure 4: Top : t-SNE embeddings of the raw series and their compact representations on Web anomaly detection. Bottom : sufficient summary plots along the first sliced inverse regression (SIR) direction ( Li, 1991 ) .

Figure 5: Confusion matrices on classification for Qwen3-8B and TimesJev. Each row shows the share of a true activity assigned to each predicted activity.

Appendix figures & tables10 assets

Supplementary material from the paper’s appendix.

Appendix

Task	Dataset	Opt.	Len.	$M$	Train	Test	Time
Anomaly Detection	4 domains	2	8–64	69	13,787	7,000	87.8
Classification	HAR	6	30	240	4,000	2,000	28.7
MCQA	Mixed	2–4	4–48	69	4,000	2,000	13.1
True/False QA	Mixed	2	4–48	69	3,703	2,000	10.5

Appendix

Table 3: Datasets and training statistics. All datasets come from TSQA; Mixed covers its five domains. Anomaly detection trains one model per domain, so its counts and time are summed over the four domains. Opt. is the number of options. Len. is the number of values in a question’s series.

M

is the size of the feature library. Time is the training time in minutes.

Setting	Value
Backbone	Decider-2B, frozen
Trainable module	Value-token MLP (0.54M)
Features	$K=32$ , stochastic gates
Gates	$\sigma=0.5$ , $\lambda\in\{0.003,0.01,0.03,0.1\}$
Optimizer	AdamW, lr $10^{-3}$ , weight decay 0
Schedule	5% warm-up, cosine decay, 3 epochs

Appendix

Table 4: Training settings, shared by all datasets.

Figure 6: Accuracy of TimesJev with the Decider-2B versus the number of selected features

K

Figure 7: Comparison of stochastic gates with other dimension reduction methods.

Figure 8: Share of the features selected by stochastic gates that come from each family of the library.

Variant	Anomaly Detection				Classification	MCQA				True/False QA				Overall
Variant	Environment	AIOps	Healthcare	Web	Human Activity	Healthcare	Nature	Web	Other	Healthcare	Nature	Web	Other	Overall
Ours (Full)	84.20	99.00	99.60	97.60	80.00	71.74	84.16	91.80	74.42	81.81	82.79	90.98	93.10	87.02
Compression
w/o STG selection	82.00	98.60	94.60	87.40	74.40	57.97	55.83	73.77	53.49	61.89	57.38	61.90	58.62	70.60
w/o subset constraint	82.20	98.80	96.60	92.40	71.20	50.00	50.00	62.30	60.47	67.13	82.25	90.48	79.31	75.63
w/o compression	73.60	98.60	99.40	78.80	71.80	57.97	65.83	85.25	72.09	72.03	80.70	88.89	89.66	79.59

Appendix

Table 5: Ablation study of TimesJev with Decider-2B on TSQA in accuracy (%). Bold denotes the best results.

Figure 9: Accuracy drop when each of the top two features is permuted across questions.

Figure 10: Case study on human activity classification.

Figure 11: Case study on anomaly detection in the environment domain.

Figure 12: Case study on anomaly detection in the web domain.

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Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis. However, time series lie outside their native textual modality, raising a fundamental question: how should time series be represented so that LLMs can reason about them effectively? Existing work typically serializes raw numerical sequences or fine-tunes pre-trained LLMs on time-series data. These approaches place the burden of extracting temporal structure directly on the LLM, creating a modality mismatch that often degrades performance on long sequences and introduces substantial computational overhead. In this work, we introduce Time-Series-to-Structured-Program representation (T2SP), a deterministic, training-free method that represents a time series as a structured symbolic program. T2SP decomposes time series into trends, periods, and salient events, expressing them in a program-friendly format aligned with the textual and code-like modalities on which LLMs are natively trained. By shifting temporal-structure extraction from the model to the representation itself, T2SP enables off-the-shelf LLMs to leverage their existing reasoning capabilities for time-series understanding. We evaluate T2SP on three reasoning tasks -- editing, captioning, and question answering -- where it consistently improves performance, reduces reasoning time, and lowers failure rates compared with raw-string representations. Our results demonstrate that T2SP provides an effective interface between time series and LLMs.

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