MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysis

1 INTRODUCTION

• Time-series analysis supports monitoring, prediction, anomaly detection, and data-driven decisions across fields, but complex dependencies and diverse tasks challenge effective analysis.The introduction calls for a unified, generalizable framework.Source: §1 · p. 1
• Visual inspection motivates the approach: anomalies can form salient regions, while recurring waveform motifs can indicate a class.The paper asks whether integrating visual representations can mimic this human-like perception and enhance model performance.Source: §1 · p. 1
• LLM-based time-series methods face a continuous-to-discrete modality mismatch, patch-size tradeoffs, and neglect of cross-channel dependencies in channel-independent approaches.Large patches can lose temporal detail, while small patches can miss global patterns.Source: §1 · p. 2
• MLLM4TS adds a vision branch that turns multivariate series into color-coded line plots and fuses visual and time-series embeddings.The fusion is intended to combine fine-grained temporal dynamics with high-level context.Source: §1 · p. 2
• The framework introduces temporal-aware visual patch alignment and reports promising performance across classification, anomaly detection, forecasting, and few-shot and zero-shot settings.The contributions also describe bridging modalities and mitigating sensitivity to patch-size selection.Source: §1 · p. 2

Traditional Time Series Methods

• Traditional methods can struggle with multivariate or high-dimensional data, while deep learning, especially Transformers, improves modeling of long-range temporal dependencies.The review names ARIMA, Exponential Smoothing, and Matrix Profile among traditional methods.Source: §2 · p. 2

LLMs for Time Series

• LLM time-series adaptations use prompting and patch-based tokenization, but face modality mismatch and difficulty capturing global trends, local details, and cross-channel dependencies.The review cites Gruver et al. (2023), Nie et al. (2022), and Zhou et al. (2023) for bridging techniques.Source: §2 · p. 3

Pretrained Time Series Models

• Pretrained time-series models such as Chronos and Moirai remain largely forecasting-focused, while MOMENT's channel-independent design hinders cross-channel modeling.The review argues that task- and modality-specific orientations limit broader applicability.Source: §2 · p. 3

Multimodal Time-Series Analysis

• Existing visual time-series approaches address individual tasks, leaving a gap for a unified framework across classification, anomaly detection, and forecasting.The review cites image-based ViTST and vision-language approaches including VisionTS, Time-VLM, and TAMA as task-specific examples.Source: §2 · p. 3

3 MLLM4TS FRAMEWORK

• The framework comprises input, embedding, language-model, and output components.Figure 2 depicts the flow from patch tokenization and color-coded plots through embedding fusion, a pretrained LLM, and a task-specific head.Source: §3 · p. 3

3.1 MODEL ARCHITECTURE AND COMPONENTS

• The input module renders each channel as a uniquely colored line plot in a horizontally stacked composite image, while sending the raw series to a tokenizer.When channels are numerous, the visualization can discard highly correlated channels while retaining the full series in the raw branch.Source: §3.1 · pp. 3–4
• The embedding module encodes modalities separately: the time-series branch normalizes and patches the raw series, while a frozen pretrained vision encoder processes the plot image through a projection layer.The projection adapts visual embeddings for compatibility with the language model.Source: §3.1 · p. 4
• Multimodal fusion combines fine-grained temporal patterns with global cross-channel information, and a task-specific output head supports classification, anomaly detection, and forecasting.Early fusion combines embeddings before LLM processing; late fusion combines them after separate LLM encoding.Source: §3.1 · p. 4

3.2 TEMPORAL-AWARE VISUAL PATCH ALIGNMENT

• Temporal-aware alignment groups visual patches sharing a horizontal index because that axis corresponds to absolute time, then aggregates each group into a visual sequence.The time-series patch size is set to L/r, and interpolation matches the visual sequence to the time-series embedding's temporal resolution.Source: §3.2 · p. 5

A Motivating Example. Figure 3 presents a motivating example comparing input modalities for time-series classification on five randomly sampled UEA datasets (Bagnall et al., 2018).

• The multimodal approach consistently outperforms unimodal baselines on five randomly sampled UEA classification datasets.The comparison covers time-series-only, plot-only, and multimodal inputs.Source: A Motivating Example. Figure 3… · p. 5, Figure 3 · p. 5

4.1.1 TIME-SERIES CLASSIFICATION

• MLLM4TS outperforms classification baselines, with time-series tokenization and vision embeddings capturing local temporal features and global cross-channel dependencies.The authors report improved accuracy across most datasets.Source: §4.1.1 · p. 6, Figure 4 · p. 6

4.1.2 TIME-SERIES ANOMALY DETECTION

• MLLM4TS achieves the best overall multivariate anomaly-detection performance, surpassing its time-series-only counterpart OFA and statistical and neural-network baselines.Table 1 uses VUS-PR as its primary measure, with higher values indicating better performance.Source: §4.1.2 · p. 6, Table 1 · p. 6

4.1.3 TIME-SERIES FORECASTING

• MLLM4TS achieves competitive forecasting results, with clear advantages on datasets exhibiting periodic patterns such as Solar-Energy.Visualizing 50 rather than all channels improves performance on Electricity (321 channels) and Traffic (862 channels), while preserving inter-channel interactions.Source: §4.1.3 · p. 7, Table 2 · p. 7

4.1.4 FEW/ZERO-SHOT LEARNING

• MLLM4TS outperforms its time-series-only counterpart in both few-shot and zero-shot settings, and exceeds numerical-only pretrained time-series foundation models in zero-shot evaluation.The authors report rapid adaptation to previously unseen datasets and resilience to distribution shifts.Source: §4.1.4 · p. 7, Figure 5 · p. 7

4.2 VISUAL REPRESENTATION ANALYSIS

• Horizontal layouts outperform grid layouts, CLIP outperforms ResNet, early fusion performs better at lower computational cost, and MLLM4TS is less sensitive to patch size.For anomaly detection, patch-size variation is not applicable because each input is a fixed-length time-series window.Source: §4.2 · pp. 7–8, Table 3 · p. 8, Figure 6 · p. 9

4.3 LANGUAGE BACKBONE ANALYSIS

• 2.6% improvement in time-series-only forecasting followed replacing the LLM with simpler attention, while language models benefit all three multimodal tasks.Billion-parameter models do not consistently improve over smaller architectures such as GPT-2; TuneAll variants add computational cost without improving performance, while MLLM4TS achieves the best overall performance with relatively low training cost.Source: §4.3 · pp. 8–9

4.4 DISCUSSION

• The discussion concludes that visual representations can enhance time-series analysis without external information or domain-specific expert knowledge, but add computational overhead.It identifies lightweight visual encoders tailored to time series as a future-work direction.Source: §4.4 · p. 9

5 CONCLUSION

• MLLM4TS combines sequential and visual representations to capture local temporal patterns and global cross-channel dependencies.The conclusion describes this as addressing complexities of multivariate time series.Source: §5 · p. 9
• The framework is evaluated on classification, anomaly detection, and forecasting, with numerical and visual modalities making complementary contributions.Source: §5 · p. 9
• The conclusion identifies aligned modalities such as images and videos, lightweight encoders, and improved visual representations as directions for future work.Source: §5 · p. 9

REPRODUCIBILITY STATEMENT

• Reproducibility materials include dataset, preprocessing, split, architecture, implementation, protocol, and metric details across the appendices and main text.Source: REPRODUCIBILITY STATEMENT · p. 10

A.1 DATASET DESCRIPTION

• Classification and forecasting follow TimesNet data-processing and train-validation-test split protocols, while anomaly detection uses the TSB-AD benchmark.Source: §A.1 · p. 17
• Tables 5–7 summarize datasets for classification, anomaly detection, and forecasting, including task-specific sample, series, and forecasting characteristics.Source: Table 5 · p. 17, Table 6 · p. 17, Table 7 · p. 18

A.2 IMPLEMENTATION DETAILS

• Each channel becomes a color-coded line plot in one composite image, while the original series and image enter separate tokenization and vision-encoding branches.Source: §A.2 · p. 18
• Temporal and visual embeddings are aligned and fused before a language-model backbone and task-specific prediction head process them.Source: §A.2 · p. 18
• Anomaly detection reconstructs the input series, forecasting predicts future values, and cross-channel weight sharing supports varying channel counts.Source: §A.2 · p. 18
• Experiments use CLIP-ViT-L-14 and GPT-2 by default, run on NVIDIA A100 GPUs, and average results over five random-seed runs.Classification allows up to 50 epochs; anomaly detection and forecasting allow up to 10 epochs.Source: §A.2 · p. 18

B SUPPLEMENTARY RESULTS

• The supplementary evaluation covers performance variability, mainstream time-series tasks, and detailed ablation results.Source: B SUPPLEMENTARY RESULTS · p. 18
• Algorithm 1 arranges each channel as a color-coded plot and renders the plots into one image tensor.Source: B SUPPLEMENTARY RESULTS · p. 19
• It projects vision-encoder features into dimension d, while time-series inputs are normalized, transposed, padded, unfolded into patches, and tokenized into dimension d; the language model returns last hidden states.Source: B SUPPLEMENTARY RESULTS · p. 19
• The task-specific head produces classification logits, anomaly reconstructions, or forecast series from the final hidden states.Source: B SUPPLEMENTARY RESULTS · p. 20

B.1 ERROR BARS

• Low standard deviation across all four TSB-AD evaluation measures suggests stable and reliable performance across five random seeds.Figure 7 covers 180 time series; it marks the mean with a dashed line and the median with a solid line.Source: §B.1 · p. 20, Figure 7 · p. 20

• Classification results on UEA datasets are reported in Table 8.Source: §B.2 · p. 20
• Anomaly-detection results are reported in Tables 9 and 10.Source: §B.2 · p. 20
• Forecasting results, including few-shot and zero-shot settings, are reported in Tables 11–13.Source: §B.2 · p. 20

B.3 ABLATION STUDY RESULTS

• MLLM4TS consistently improves over its time-series-only counterpart, and ablations across variants support the framework’s robustness.The reported studies span classification, anomaly detection, and forecasting.Source: §B.3 · pp. 20, 23
• Autoregressive forecasting performs well at short horizons but degrades as the window lengthens, while scaling from GPT-2 (124M) to Qwen3 (1.7B) yields no further gains.The authors suggest larger models may be more prone to overfitting time-series noise.Source: §B.3 · p. 23

C SHOW CASE

• The showcase section presents example time-series plots and language-model attention maps for multimodal tokens.Source: C SHOW CASE · p. 23

C.1 EXAMPLE PLOTS

• The examples contrast horizontal and grid layouts and show plots from datasets used in the work.Figure 8 compares GPT-2 and Qwen3 across classification, anomaly detection, and forecasting; Table 17 describes channel reduction to 50 plotted channels.Source: §C.1 · p. 23, Figure 8 · p. 24, Figure 9 · p. 25

C.2 ATTENTION MAPS

• Attention shifts from primarily time-series tokens in early layers toward a more even distribution across time-series and visual tokens in deeper layers.Figure 11 averages attention weights over the test set.Source: §C.2 · p. 27, Figure 11 · p. 27

C.3 IMPACT OF CHANNEL COLOR CODING

• Color-coded channels outperform the uncolored visual version, which performs between color-coded MLLM4TS and the time-series-only baseline.The authors interpret this ordering as evidence that channel-wise color coding helps capture cross-channel dependencies.Source: §C.3 · p. 27, Figure 12 · p. 28

D BROADER IMPACT

• The paper frames MLLM4TS as relevant to decision-support applications and proposes extending its line-plot focus to aligned image and video data.It identifies healthcare, manufacturing, finance, and climate-related services as potential application domains.Source: D BROADER IMPACT · pp. 27–28

• The vision branch adds computational overhead by increasing runtime while visual embeddings are processed.Source: E LIMITATION AND FUTURE WORK · p. 28
• The authors propose lightweight visual frontends as a way to improve runtime efficiency.Source: E LIMITATION AND FUTURE WORK · p. 28
• Extending the framework to irregularly sampled time series is identified as a future direction, potentially using image representations.Source: E LIMITATION AND FUTURE WORK · p. 28