Time series data are integral to applications across domains such as finance, healthcare, transportation, and environmental science.While recent work has begun to explore time series question answering (QA), existing benchmarks still provide limited coverage of analytical capabilities under a standardized evaluation framework. We introduce TSAQA, a novel unified benchmark designed to broaden task coverage and evaluate diverse temporal analysis capabilities. TSAQA integrates 6 diverse tasks under a single framework ranging fromconventional analysis, including anomaly detection and classification, to advanced analysis, such as characterization, comparison, datatransformation, and temporal relationship analysis. Spanning 210k samples across 13 domains, the dataset employs diverse formats, including true-or-false (TF), multiple-choice (MC), and a novel puzzling (PZ), to comprehensively assess time series analysis. Zero-shotevaluation shows that TSAQA remains challenging for current Large Language Models (LLMs): best-performing commercial model,Gemini-2.5-Flash, achieves 65.08 average accuracy. Although instruction tuning improves open-source models’ performance: the best-performing model, LLaMA-3.1-8B, shows significant room for improvement. We further evaluate language-capable time series foundation models (TSFMs), showing that TSAQA extends beyond general-purpose LLMs. The data are available in https://huggingface.co/datasets/TSAQA/TSAQA-Benchmark.

How an economic shock propagates through the heterogeneous decisions of diverse economic roles and emerges as aggregate fluctuations is a central question in economics. Traditional agent-based models hard-code behavioral rules and cannot read language, while recent LLM-agent simulations often omit explicit belief formation, endogenous price formation, or mechanism-grounded state transitions, and rarely let a human step into the loop. We present WonderEcon, a configurable, LLM-driven yet economic-rule-grounded multi-agent simulation system in which households, firms, commercial banks, a central bank, and a government interact across five markets, forming a self-consistent macroeconomic loop. Every agent decides through a four-step natural-language pipeline, reading real news alongside structured data, with every step observable. A player mode, with dedicated scene and character modeling, lets a human take over one household agent, yielding a counterfactual comparison and the Economic Personality Type Indicator (EPTI). On an interest-rate hiking cycle, WonderEcon demonstrates directional plausibility with respect to six well-known economic principles across four LLMs, and 33 participants find the player system satisfying. The engine and interfaces are publicly available.