README.md

---
license: mit
task_categories:
- question-answering
- time-series-forecasting
language:
- en
tags:
- Time Series
- Time Series QA
- Time Series Analysis
- Time Series Reasoning
- Time Series Question Answering
- Unified Time Series QA
- TSQA
size_categories:
- 100K<n<1M
---
# Time Series Analysis Question Answering Benchmark (TSAQA)

View our paper at: https://arxiv.org/abs/2601.23204  

## Introduction

TSAQA is 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 from Conventional Analysis, including anomaly detection and classification, to Advanced Analysis, such as characterization, comparison, data transformation, 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.

This benchmark allows development of Large Language Models (LLMs) and Time Series Foundation Models (TSFM) specifically for time series analysis and time series reasoning.

<p align="center">
  <img src="figs/final_conventional.png" alt="Illustration of Conventional Tasks" width="58%">
</p>
<p align="center">
  <img src="figs/final_reasoning.png" alt="Illustration of Advanced Tasks" width="99%">
</p>
<p align="center">
  <b>Figure:</b> Data distribution and tasks of TSAQA.
</p>

### 🧩 Tasks of TSAQA. TF, MC, and PZ denote true-or-false, multiple-choice, and puzzling.

| **Group**              | **Task**              | **Description**                                             | **Question Type** |
| ---------------------- | --------------------- | ----------------------------------------------------------- | ----------------- |
| **Conventional Tasks** | Anomaly Detection     | Determine whether the input contains anomalies.             | TF                |
|                        | Classification        | Classify the input time series.                             | MC                |
| **Advanced Tasks**     | Characterization      | Determine the characteristics of the time series.           | TF & MC           |
|                        | Comparison            | Compare the characteristics of two time series.             | TF & MC           |
|                        | Data Transformation   | Identify the relationship between raw and transformed data. | TF & MC           |
|                        | Temporal Relationship | Determine the temporal relationship of patches.             | TF & MC & PZ      |


# Data Statistics of TSAQA
<p align="center">
  <img src="figs/data_statistics.jpg" alt="Domain and Task Distribution of TSAQA" width="40%">
</p>

# 🧠 Task Groups in TSAQA
TSAQA benchmark encompasses two groups of tasks with six diverse tasks designed to evaluate a model's ability of understanding the fundamental properties of time series data. 
The **TSAQA** benchmark includes two major groups of tasks designed to evaluate different reasoning abilities in time series analysis.



### 🔹 **Conventional Analysis Tasks**

These are classic tasks widely explored in traditional time series analysis:

1. **🩸 Anomaly Detection** – Identify irregular or unexpected patterns in a time series.  
2. **🏷️ Classification** – Reason about the relationship between a time series and its underlying conceptual category.


### 🔸 **Advanced Analysis Tasks**

These novel analytical tasks focus on deeper, intrinsic properties of time series:

3. **📊 Characterization** – Infer fundamental properties such as trend, seasonality, and dispersion.  
4. **⚖️ Comparison** – Reason about relative similarities and differences between two time series.  
5. **🔄 Data Transformation** – Understand relationships between original and transformed time series (e.g., via Fourier transform).  
6. **⏱️ Temporal Relationship** – Capture chronological dependencies among time series patches.

---

> 🧩 **Insight:**  
> These **advanced analysis tasks** push the boundaries of conventional time series modeling—encouraging the development of models that can **grasp cognitive concepts of time series** and **reason over human-posed questions**.


# 📊 Data Collection

In this section, we detail the data sources, including **core datasets**, **anomaly detection datasets**, and **classification datasets**.

---

## 🧩 Core Datasets

We extract data from multiple time-series datasets, including:

**Australian Electricity Demand** — Half-hourly electricity demand for Victoria, Australia (2014).  
**BDG-2 Rat** — Building-level electricity data from the Building Data Genome Project 2.  
**GEF12** — Load forecasting benchmark from the Global Energy Forecasting Competition 2012.  
**ExchangeRate** — Daily exchange rates for currencies of eight countries (1990–2016).  
**FRED-MD** — Monthly macroeconomic indicators from the Federal Reserve Bank.  
**BIDMC32HR** — ICU PPG and ECG recordings from 53 adult patients.  
**PigArtPressure** — Vital sign data from 52 pigs pre/post induced injury.  
**US Births** — Daily number of U.S. births (1969–1988).  
**Sunspot** — Daily sunspot numbers from 1818–2020.  
**Saugeen** — Daily mean river flow data for the Saugeen River (1915–1979).  
**Subseasonal Precipitation** — Daily precipitation (1948–1978).  
**Hierarchical Sales** — SKU-level daily pasta brand sales (2014–2018).  
**M5** — Walmart hierarchical sales forecasting dataset.  
**Pedestrian Counts** — Hourly pedestrian counts from 66 sensors in Melbourne (2009–2020).  
**PEMS03** — Traffic flow data collected by Caltrans PeMS.  
**Uber TLC Daily** — Uber pickup counts in NYC (Jan–Jun 2015).  
**WikiDaily100k** — Daily traffic data for 100,000 Wikipedia pages.

### 📈 Summary of Core Datasets

| **Dataset** | **Total Data Points** | **Domain** |
|--------------|----------------------|-------------|
| AustralianElectricityDemand | 1,153,584 | Energy |
| BDG-2 Rat | 4,728,288 | Energy |
| GEF12 | 788,280 | Energy |
| ExchangeRate | 56,096 | Finance |
| FRED MD | 76,612 | Finance |
| BIDMC32HR | 8,000,000 | Healthcare |
| PigArtPressure | 624,000 | Healthcare |
| USBirths | 7,275 | Healthcare |
| Sunspot | 73,924 | Nature |
| Saugeenday | 23,711 | Nature |
| SubseasonalPrecip | 9,760,426 | Nature |
| HierarchicalSales | 212,164 | Sales |
| M5 | 58,327,370 | Sales |
| PedestrianCounts | 3,130,762 | Transport |
| PEMS03 | 9,382,464 | Transport |
| UberTLCHourly | 1,129,444 | Transport |
| WikiDaily100k | 274,099,872 | Web |

---

## 🚨 Anomaly Detection Datasets

We extract data from multiple anomaly detection benchmarks, including:

- **MGAB** – Mackey–Glass time series exhibiting chaotic behavior and synthetic anomalies.  
- **ECG** – Electrocardiogram recordings with anomalies corresponding to ventricular premature contractions.  
- **Genesis** – Spacecraft telemetry data from a pick-and-place demonstrator.  
- **GHL** – Gasoil Heating Loop data with simulated cyber-attacks.  
- **Occupancy** – Room occupancy monitoring using temperature, humidity, light, and CO₂ data.  
- **SMD** – Server Machine Dataset from a large Internet company, labeled for anomaly detection.

### 🧾 Summary of Anomaly Detection Datasets

| **Name** | **# Samples** | **Domain** |
|-----------|---------------|------------|
| ECG | 17,862 | Healthcare |
| SMD | 58,888 | Cyber-security / IT Operations |
| MGAB | 376 | Mathematical Biology |
| Genesis | 274 | Spacecraft Telemetry |
| GHL | 768 | Industrial Control System |
| Occupancy | 8,178 | Environmental Sensing |

---

## 🧠 Classification Datasets

We extract data from the **UCR Archive** using the following criteria:
- Datasets with **≤4 classes**
- Time series length **≤400** time points

A total of **37 benchmarks** were selected, spanning domains such as robotics, energy, healthcare, synthetic, manufacturing, nature, and transport.

### 🗂️ Summary of Classification Datasets

| **Name** | **# Samples** | **# Classes** | **Domain** |
|-----------|---------------|---------------|-------------|
| SonyAIBORobotSurface1 | 486 | 2 | Robotics |
| SonyAIBORobotSurface2 | 771 | 2 | Robotics |
| FreezerRegularTrain | 2,404 | 2 | Energy |
| FreezerSmallTrain | 2,353 | 2 | Energy |
| ToeSegmentation1 | 210 | 2 | Healthcare |
| ToeSegmentation2 | 129 | 2 | Healthcare |
| TwoPatterns | 3,999 | 4 | Synthetic |
| CBF | 757 | 3 | Synthetic |
| Wafer | 5,744 | 2 | Manufacturing |
| ECG200 | 159 | 2 | Healthcare |
| TwoLeadECG | 923 | 2 | Healthcare |
| ECGFiveDays | 704 | 2 | Healthcare |
| DistalPhalanxOutlineCorrect | 690 | 2 | Healthcare |
| MiddlePhalanxOutlineCorrect | 731 | 2 | Healthcare |
| ProximalPhalanxOutlineCorrect | 688 | 2 | Healthcare |
| DistalPhalanxOutlineAgeGroup | 423 | 3 | Healthcare |
| MiddlePhalanxOutlineAgeGroup | 435 | 3 | Healthcare |
| ProximalPhalanxOutlineAgeGroup | 485 | 3 | Healthcare |
| PhalangesOutlinesCorrect | 2,076 | 2 | Healthcare |
| MoteStrain | 1,012 | 2 | Nature |
| GunPointMaleVersusFemale | 362 | 2 | Healthcare |
| GunPointOldVersusYoung | 356 | 2 | Healthcare |
| GunPointAgeSpan | 368 | 2 | Healthcare |
| GunPoint | 169 | 2 | Healthcare |
| Strawberry | 786 | 2 | Nature |
| ItalyPowerDemand | 890 | 2 | Energy |
| Chinatown | 293 | 2 | Transport |
| BME | 137 | 3 | Synthetic |
| PowerCons | 294 | 2 | Energy |
| DodgersLoopWeekend | 111 | 2 | Transport |
| DodgersLoopGame | 115 | 2 | Transport |
| DiatomSizeReduction | 248 | 4 | Nature |
| SmoothSubspace | 236 | 3 | Synthetic |
| UMD | 148 | 3 | Synthetic |
| Wine | 85 | 2 | Nature |
| Coffee | 48 | 2 | Nature |
| ArrowHead | 175 | 3 | Nature |

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