Time Series Language Models (TSLMs) enable language-based understanding and reasoning over temporal data, yet a fundamental question remains: what information should their time-series representations preserve, and what variation should they factor out? Existing TSLMs typically expose the language model to a single representation, imposing a fixed inductive bias across downstream tasks. We instead introduce complementary representations as a design principle for time-series reasoning. Our model combines two visual views: line plots preserve absolute measurements and the observed coordinate frame, while normalized delay embeddings factor out positive amplitude rescaling and offsets while exposing temporal structure. Dedicated visual encoders integrate both views into a shared language model through visual tokens and multi-level feature fusion. Evaluation-time interventions verify that the two views exhibit distinct invariance profiles, while single-view ablations show that their combination is particularly beneficial beyond basic perception, with the large gains in prediction and reasoning. Built on Qwen3-VL-8B, our dual-view model improves overall TSRBench accuracy from 44.5% with line plots alone to 53.2%, substantially exceeding the strongest evaluated dedicated TSLM baseline at 36.7%. It further achieves 89.9% accuracy on TSExam and 86.8% numerical accuracy on multivariate ChatTS alignment. Together, these results demonstrate that complementary representations provide an effective design principle for general-purpose TSLMs, enabling models to exploit temporal structure without sacrificing access to the original measurement frame.
Figures & tables
Figure 1: Cross-benchmark accuracy (%). ChatTS-A and ChatTS-B report categorical (Cat.) and numerical (Num.) alignment on the univariate and multivariate datasets, respectively. Bold marks the best result in each panel, excluding GPT-5 (T+V), shown in gray, which is a large proprietary model included as a reference rather than a size-matched comparison.
Model
TS Representation
Normalization
Scale Information
Representation Views
Task Scope
Forecast.
PromptCast ( Xue and Salim, 2023 )
Text
None
In representation
Single
Forecasting
LLMTime ( Gruver et al., 2023 )
Text
Per-series rescaling
Not provided
Single
Forecasting
Time-LLM ( Jin et al., 2024 )
Reprogrammed patches
Per-series
Not provided
Single
Forecasting
TSLMs
TimeOmni-1 ( Guan et al., 2026 )
Text
None
In representation
Single
QA & reasoning
ChatTS ( Xie et al., 2025 )
1D patches
Per-series
Auxiliary metadata
Single
QA & reasoning
OpenTSLM ( Langer et al., 2026 )
1D patches
Per-series
Auxiliary metadata
Single
QA & reasoning
Table 1: Comparison of time-series representations in existing TSLMs. The methods differ in how the time series is represented, whether the primary representation is normalized, and how absolute measurement-scale information is provided to the language model. Forecasting-oriented LLM methods are included for completeness; unlike TSLMs, they target numerical forecasting rather than general question answering and reasoning.
Figure 2: Overview of the Dual-View TSLM. Each visual span is wrapped by vision_start and vision_end . Stage I trains the DINOv3 LoRA and the merger. Stage II trains the LM LoRA. The Qwen vision encoder, the text embedding, and the LLM backbone stay frozen.
Stage
Source
Origin
Rows
Supervision
I
CaTS-Bench
Real ( Zhou et al., 2026 )
15,995
Caption
I
ChatTS-style captions
Synthetic ( Xie et al., 2025 )
32,000
Caption
I
Structural captions
Synthetic, in-house
7,701
Caption
II
ChatTS ift
Synthetic ( Xie et al., 2025 )
6,373
Free-form QA
II
ChatTS sft
Synthetic ( Xie et al., 2025 )
44,802
Free-form QA
II
TSExam perception
Synthetic, in-house
20,640
MCQ
Table 2: Training-data composition. Rows denote distinct examples available to the corresponding training stage. Stage I sources are sampled with equal probability; Stage II uses a balanced mixture described in the text.
Perception
Reasoning
Prediction
Decision
Model
PR
NU
AD
CA
ER
CD
AR
TR
NR
DR
IR
TSF
EP
QuaDM
QuanDM
Overall
General
GPT-5 (V)
83.6
72.4
69.8
73.5
34.6
37.0
81.3
68.1
64.0
48.0
57.0
38.8
71.9
26.9
30.0
52.4
GPT-5 (T+V)
84.9
72.4
61.2
70.8
38.9
50.3
93.3
68.8
72.0
44.0
58.0
37.8
78.1
31.9
34.3
55.6
Qwen3-VL-8B (V)
54.7
57.5
62.8
65.5
24.3
31.7
52.0
23.1
33.2
19.2
29.0
51.7
30.6
36.1
29.0
38.9
TSLMs
ChatTS-14B
50.7
50.6
46.5
55.8
21.7
34.3
51.3
24.4
30.5
23.6
25.0
19.2
52.2
29.3
27.7
33.5
TS-Reasoner-7B
53.1
56.3
48.1
50.4
28.9
24.3
57.3
26.9
37.2
23.6
24.0
31.7
55.0
32.8
21.7
36.4
Table 3: TSRBench accuracy (%) across all 15 tasks. Rows are grouped into general-purpose models, dedicated TSLMs, and representation ablations. In the Overall column, bold and underline mark the best and second-best results, respectively. Gray rows denote large proprietary models included as reference points rather than size-matched comparisons and are excluded from this ranking. Full task descriptions and results are provided in Appendix C .
Affine
Noise / Smoothing
Sampling
Model
Clean
×0.1
×10
+5σ
−5σ
z -score
20 dB
10 dB
0 dB
Smooth
256
2048
Plot only
52.7
58.2
49.6
48.9
49.2
47.5
50.6
47.4
43.2
50.1
51.6
52.9
Delay only
44.7
43.4
44.9
44.9
44.1
44.3
42.3
38.3
35.3
40.7
45.1
42.8
Ours
66.5
63.4
64.8
62.9
65.0
60.7
65.4
62.4
56.6
60.7
64.8
66.8
Table 4: Evaluation-time interventions on scale- and sampling-invariant TSRBench subsets (2,020 items; excludes NR, DR, IR, TSF, and decision tasks). Each entry reports accuracy (%).
Appendix figures & tables16 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 3: Dual-view visual representations of a time-series variate. (a) The line plot retains the original measurement frame and axis values. (b) The normalized delay map arranges overlapping windows as columns, so recurring structure appears as diagonal stripes.
Symbol
Description
Value
ρ
Window length as a fraction of series length
0.6
ℓmin
Minimum window length
48
ℓmax
Maximum window length
192
W
Number of windows (map width)
256
H
Resampled window length (map height)
256
Appendix
Table 5: Hyperparameters of the normalized delay-embedding view.
TSExam
ChatTS-A
ChatTS-B
ChatTS
TSRBench
Model
Overall
Cat.
Num.
Cat.
Num.
Reason.
Overall
Delay only
0.749
0.782
0.290
0.806
0.308
0.596
0.405
Plot only
0.899
0.861
0.781
0.895
0.864
0.613
0.452
Delay+Delay
0.790
0.858
0.424
0.848
0.348
0.604
0.426
Plot+Plot
0.865
0.873
0.805
0.877
0.859
0.586
0.448
Ours
0.899
0.864
0.805
0.893
0.868
0.615
0.532
Appendix
Table 6: Cross-benchmark representation ablations. ChatTS-A and ChatTS-B report categorical (Cat.) and numerical (Num.) alignment accuracy. ChatTS Reason. reports average accuracy across the four reasoning evaluations. TSRBench reports overall accuracy. Plot+Plot and Delay+Delay feed the same representation to both towers while matching the architecture and visual-token budget of our model.
Model
Perception
Reasoning
Prediction
Decision
Overall
Delay only
63.7
36.5
39.4
27.6
40.5
Plot only
87.4
35.8
38.0
36.4
45.2
Delay+Delay
71.6
37.0
41.7
27.4
42.6
Plot+Plot
85.3
36.3
40.3
30.7
44.8
Ours
86.4
40.1
61.7
37.6
53.2
Appendix
Table 7: TSRBench category-level accuracy (%) for the representation ablations. Overall accuracy is computed using the benchmark’s complete task-level evaluation.
Model
Induct.
Deduct.
Causal
MCQ2
Average
Delay only
0.403
0.605
0.857
0.520
0.596
Plot only
0.441
0.548
0.851
0.610
0.613
Ours
0.479
0.535
0.826
0.620
0.615
Appendix
Table 8: ChatTS reasoning accuracy for the single-view and dual-view models on inductive, deductive, and causal reasoning and two-series multiple-choice questions (MCQ2). Inductive reasoning is scored with the RAGAS pipeline; Average reports the mean over the four tasks.
Plot only
Ours
Delay only
Intervention Type
Transformation
Acc.
Δ vs. Clean
Acc.
Δ vs. Clean
Acc.
Δ vs. Clean
Baseline
Clean
0.452
—
0.532
—
0.405
—
Affine Invariance
Scale ×0.1
0.467
+1.5
0.496
−3.6
0.407
+0.2
Scale ×10
0.439
−1.4
0.522
−1.0
0.411
+0.6
Offset +5σ
0.431
−2.1
0.511
−2.2
0.397
−0.8
Offset −5σ
0.430
−2.2
0.520
−1.3
0.400
−0.6
Appendix
Table 9: Evaluation-time affine and dynamical interventions across the full TSRBench benchmark (all 15 tasks, including forecasting). For each model, we report clean accuracy and the percentage-point change ( Δ ) relative to clean performance. Daggers ( † ) mark non-invariance interventions for which gold targets legitimately change. Bold values indicate the smallest absolute shift ( ∣Δ∣ ) in each row.
Secondary Representation
Only
+ Plot
STFT ( z -normalized)
0.739
0.889
STFT (raw)
0.745
0.894
Delay
0.737
0.893
Appendix
Table 10: Controlled TSExam comparison of alternative secondary representations. “Only” evaluates the secondary representation without the line-plot stream, whereas “+ Plot” combines it with the line plot.
STFT ( z -normalized)
STFT (raw)
Delay
Intervention Type
Transformation
Acc.
Δ vs. Clean
Acc.
Δ vs. Clean
Acc.
Δ vs. Clean
Baseline
Clean
0.739
—
0.745
—
0.737
—
Affine Invariance
Scale ×0.1
0.725
−1.3
0.727
−1.9
0.733
−0.4
Scale ×10
0.728
−1.1
0.727
−1.9
0.731
−0.7
Offset +5σ
0.728
−1.1
0.634
−11.1
0.732
−0.5
Offset −5σ
0.729
−0.9
0.649
−9.7
0.731
−0.7
Appendix
Table 11: Evaluation-time affine and dynamical interventions on TSExam ( N=746 ) for the single-representation models. For each model, we report clean accuracy and the percentage-point change ( Δ ) relative to clean performance. Daggers ( † ) mark non-invariance interventions for which gold targets legitimately change. Bold values indicate the smallest absolute shift ( ∣Δ∣ ) in each row.
ChatTS-A
ChatTS-B
Model
TSExam
Cat.
Num.
Cat.
Num.
Reason.
TSRBench
Plot + STFT
0.890
0.859
0.809
0.894
0.871
0.628
0.467
Plot + Delay (Ours)
0.899
0.864
0.805
0.893
0.868
0.615
0.532
Appendix
Table 12: Full-training comparison between the proposed delay-based dual-view model and an alternative model using STFT as the secondary representation. ChatTS-A and ChatTS-B report categorical (Cat.) and numerical (Num.) alignment accuracy; Reason. reports the ChatTS reasoning average.
Perception
Reasoning
Prediction
Decision
Model
Perc.
PR
NU
AD
CA
Reas.
ER
CD
AR
TR
NR
DR
IR
Pred.
TSF
EP
Dec.
QualDM
QuantDM
Overall
Textual Time Series as Input
DeepSeek-V3.2
63.9
67.7
56.3
57.4
64.6
35.6
32.0
35.7
70.7
19.4
27.3
47.6
24.0
33.4
26.5
47.2
30.8
33.1
28.3
39.1
GPT-OSS-120B
64.6
66.8
56.3
59.8
69.0
38.5
39.1
26.3
58.7
31.3
48.5
28.0
40.0
40.6
31.1
59.7
32.4
33.7
31.0
42.0
Qwen3-235B-A22B
63.8
66.0
56.3
59.7
67.3
40.9
22.6
34.7
86.7
28.1
44.8
49.2
39.0
35.6
29.0
48.9
32.5
34.8
30.0
42.2
GPT-5-mini
68.7
72.2
59.8
63.6
69.9
47.5
45.1
27.7
62.7
39.4
65.2
41.3
50.0
38.3
23.5
67.8
33.0
35.5
30.3
46.6
Appendix
Table 13: TSRBench accuracy (%) by input modality. Bold columns (Perc., Reas., Pred., Dec.) are item-weighted category averages over their sub-tasks. Gray rows denote reference models (proprietary or open-weight models larger than 72B parameters).
Dataset
Model
Trend
Season
Noise
Local
Corr.
Clus.
Overall
Cate.
Num.
Cate.
Num.
Cate.
Num.
Cate.
Num.
Cate.
Cate.
Cate.
Num.
A
GPT-4o (T)
0.585
0.882
0.811
0.768
0.905
0.153
0.379
0.256
0.476
0.333
0.542
0.371
GPT-4o (V)
0.659
0.613
0.811
0.559
0.810
0.248
0.537
0.414
0.476
0.480
0.609
0.436
GPT-5 (T)
0.512
0.887
0.973
0.000
0.905
0.350
0.905
0.741
0.881
0.485
0.797
0.724
GPT-5 (V)
0.732
0.646
0.973
0.000
0.762
0.394
0.874
0.725
0.857
0.600
0.810
0.670
GPT-5 (T+V)
0.585
0.905
0.973
0.000
0.905
0.423
0.863
0.735
0.857
0.558
0.801
0.731
Appendix
Table 14: ChatTS alignment benchmark on univariate (Dataset A) and multivariate (Dataset B) series. Gray rows denote reference models. Corr. and Clus. are the correlation and clustering sub-tasks (categorical only). Bold values mark the best result per column within each dataset, excluding reference models.
Model
Induct.
Deduct.
Causal
MCQ2
Average
GPT-4o (T)
0.336
0.628
0.685
0.510
0.540
GPT-4o (V)
0.322
0.605
0.652
0.490
0.517
GPT-5 (T)
0.277
0.581
0.663
0.730
0.563
GPT-5 (V)
0.382
0.674
0.707
0.675
0.610
GPT-5 (T+V)
0.315
0.605
0.717
0.760
0.599
Qwen3-VL-8B (V)
0.197
0.558
0.685
0.480
0.480
Appendix
Table 15: ChatTS reasoning evaluation (Dataset A). Inductive reasoning is free-form Q&A scored with the RAGAS pipeline; the other tasks are multiple-choice or true/false accuracy. MCQ2 samples 100 questions from the two-series set. Gray rows denote reference models. Bold values mark the best result per column, excluding reference models.
Model
Overall
Anomaly
Causality
Noise
Pattern
Similarity
GPT-4o (T)
0.586
0.491
0.458
0.667
0.594
0.667
GPT-4o (V)
0.736
0.583
0.611
0.786
0.787
0.758
GPT-5 (T)
0.724
0.704
0.556
0.679
0.782
0.700
GPT-5 (V)
0.784
0.648
0.431
0.750
0.906
0.775
GPT-5 (T+V)
0.808
0.731
0.417
0.738
0.931
0.792
Qwen3-VL-8B (V)
0.618
0.546
0.514
0.643
0.630
0.692
Appendix
Table 16: TSExam accuracy across the 746 questions, by category. Gray rows denote reference models. Bold values mark the best result per column, excluding reference models.
Stage
Source
Raw Examples
Effective Draws / Epoch
Supervision
I
CaTS
15,995
∼ 18,565
Caption / description
I
ChatTS-style captions
32,000
∼ 18,565
Caption / description
I
Structural captions
7,701
∼ 18,565
Caption / description
Stage I total
55,696
55,696
II
ChatTS IFT
6,373
∼ 23,938
Instruction following
II
ChatTS SFT
44,802
∼ 23,938
Instruction following
Appendix
Table 17: Training-data composition. Raw examples denote the number of available examples before sampling, whereas effective draws denote the approximate number presented in one training epoch. Stage-I sources are sampled with equal source probability. In Stage II, the reasoning and numerical corpora retain their natural sizes, while the remaining sampling budget is divided approximately equally among the three instruction/multiple-choice sources.
Five-channel synthetic weather traces in which rainfall events are triggered by a latent humidity surge and barometric-pressure drop; two informative and three nuisance channels.
5
2
Rationale + Letter
ER
2
14,802
14.8%
Parameterized light curves spanning five variable-star classes and tri-axial accelerometer segments spanning six physical-activity classes.
1 / 3
5 / 6
Option Letter
CD
1
12,687
12.7%
Random five-node directed acyclic graphs simulating sensor networks across heterogeneous ranges; the task is to identify the generating adjacency structure.
5
4
Option Letter
TSF
1
12,500
12.5%
Histories containing trend, seasonality, and autoregressive structure; the task is to select the appropriate continuation trajectory among generated alternatives.
4
4
Option Letter
DR
3
10,573
10.6%
Propagation of dynamical processes including AR(1), geometric growth, and continuous Lorenz-attractor state trajectories.
4
4
Option Letter
Appendix
Table 18: Taxonomy of the reasoning corpus (100,000 examples across 25 templates). Shares are computed over the complete reasoning corpus. Where a family mixes sources, Channels and Options are listed per source in the order of the description.
Subcategory
Tmpl.
Items
Share
Target Statistic
Channels
Options
Output
Statistical retrieval
10
5,500
50.0%
Maximum, minimum, mean, median, first and third quartile, standard deviation, variance, first value, and last value.
1
—
Scalar
Signal properties
5
2,750
25.0%
Range, interquartile range, mean absolute value, lag-1 autocorrelation, and number of zero crossings.
1
—
Scalar
Step dynamics
5
2,750
25.0%
Mean absolute first difference, largest one-step jump, best-fit slope, first-half minus second-half mean, and energy.
1
—
Scalar
Appendix
Table 19: Taxonomy of the numerical corpus (11,000 examples across 20 templates). Shares are computed over the complete numerical corpus.
Time Series Language Models (TSLMs) offer a promising path toward time series understanding by reasoning over temporal signals and producing natural language answers and explanations. A common approach is Chain-of-Thought (CoT), which generates step-by-step rationales linking relevant signal patterns to final answers. Although these models learn from reference CoT traces during post-training, generating faithful descriptions of input time series at inference remains challenging. Expressing high-dimensional, continuous temporal representations in discrete language tokens may cause the model to neglect task-relevant patterns or describe them inaccurately. Because later reasoning steps build on these descriptions, early errors propagate, leading to incorrect answers with plausible explanations that are inconsistent with the input signal. We propose Lapras (Latent Post-trained Reasoning Across Series), a post-training framework that equips TSLMs with latent reasoning. A model trained with Lapras reasons through a sequence of continuous thoughts in the joint time series-language space, producing text only for the final answer. It learns this through teacher-student self-distillation, where a teacher trained on CoT reference traces reasons explicitly through text. The student aligns its hidden states with the teacher's at the answer stage, transferring the teacher's reasoning ability into its latent computation. We evaluate Lapras across four TSLM backbones on five time series question answering benchmarks. Lapras improves average F1 by up to 10.79% over explicit CoT while generating 23.9x fewer tokens. Lapras's continuous thoughts can also be decoded into readable reasoning traces via standard language decoding, preserving textual explanations. Together, these results highlight Lapras as a promising post-training paradigm for efficient, effective, and interpretable TSLM reasoning.
Yuliang Chen, Yu Yvonne Wu, Patrick Langer +11
Dartmouth College · Aionic Labs · Agentic Systems Lab, ETH Zurich +2
Comprehensive understanding of time series remains a significant challenge for Large Language Models (LLMs). Current research is hindered by fragmented task definitions and benchmarks with inherent ambiguities, precluding rigorous evaluation and the development of unified Time Series Reasoning Models(TSRMs). To bridge this gap, we formalize Time Series Reasoning (TSR) via a four-level taxonomy of increasing cognitive complexity. We introduce HiTSR, a hierarchical time series reasoning dataset comprising 83k samples with diverse task combinations and verified Chain-of-Thought (CoT) trajectories. Leveraging HiTSR, we propose LLaTiSA, a strong TSRM that integrates visualized patterns with precision-calibrated numerical tables to enhance the temporal perception of Vision-Language Models (VLMs). Through a multi-stage curriculum fine-tuning strategy, LLaTiSA achieves superior performance and exhibits robust out-of-distribution generalization across diverse TSR tasks and real-world scenarios. Our code is available at https://github.com/RainingNovember/LLaTiSA.
Time series analysis has recently been coupled with Large Language Models (LLMs) to leverage their reasoning and world knowledge capabilities, yet gains remain limited. We attribute this to a fundamental mismatch between existing task formulations and LLM strengths: most settings reduce time series understanding to curve-fitting systems, focusing on low-level prediction while ignoring the semantic, contextual, and reasoning-intensive nature of real-world temporal decision-making.To address these limitations, we introduce TSCognition, a multimodal benchmark for multi-dimensional time series reasoning. It collects real-world time series and textual information from 15 public sources and constructs approximately 41K QA samples around five cognitive reasoning tasks: Decoding, Grounding, Inferring, Extrapolating, and Acting. Building on this, we further propose TSAlign, a unified framework that encodes time series into compact patch-level representations and aligns them with semantic directions in the LLM embedding space via gated residual injection and multivariate fusion.Experiments show that TSAlign outperforms existing LLM, VLM, and time series QA baselines on TSCognition and the publicly available TimerBed benchmark while substantially reducing computational cost.Code is available at: https://github.com/EIT-NLP/CognitiveTSR
Xin Qiu, Junlong Tong, Yao Zhang +3
Institute of Digital Twin, Eastern Institute of Technology, Ningbo · Zhejiang University · Munich Center for Machine Learning, LMU