Combining multiple complementary representations of the same time-series data—each with different invariance properties—helps language models better capture both absolute values and temporal structure, improving performance on prediction and reasoning tasks.
This paper proposes using two complementary visual representations for time-series reasoning in language models: line plots that preserve raw measurements, and normalized delay embeddings that expose temporal patterns. A dual-view model combining both through visual encoders significantly outperforms single-representation baselines on time-series understanding benchmarks.