---
title: 'Time-300B: Multi-Domain Time Series Dataset'
url: https://www.emergentmind.com/topics/time-300b-dataset
type: topic
---

# Time-300B: Multi-Domain Time Series Dataset

Time-300B is a large-scale, multi-domain time series dataset developed to enable the pre-training of foundation models for forecasting tasks across diverse modalities, resolutions, and real-world environments. Designed explicitly to support zero-shot and multi-domain generalization, Time-300B serves as the backbone for major advancements in time series foundation models, including the Cisco Time Series Model and Time-MoE, and is characterized by its scale, heterogeneous sources, multiresolution context structure, sophisticated preprocessing, and open-access ethos [2511.19841][2409.16040].

## 1. Composition and Scale

Time-300B consists of over 300 billion unique time-stamped observations spanning millions of individual time series collected or synthesized across several domains. Its construction synthesizes three principal sources:

- **Proprietary observability time series:**  
  Approximately 400 million distinct time series collected over 13 months at 1-minute (majority) and 5-minute resolutions, yielding an initial pool of ∼3.1 × 10¹² raw minute-level samples before windowing. After filtering and windowing, these contribute ∼35% (1-minute) and 16.5% (5-minute) of the final points [2511.19841].

- **Public multi-domain benchmarks:**  
  - GIFT-Eval: 4.5 million series, 230 billion points (∼29.5% of final pool); domains include traffic, finance, energy, and web [2511.19841].
  - Chronos: 0.9 million series, 85 billion points (∼4.5% of pool).
  - Additional datasets summarized in nine domain categories: Energy, Finance, Healthcare, Nature, Sales, Synthetic, Transport, Web, and Other. For instance, the Nature domain (with sources like ERA5, CMIP6, and WeatherBench) represents 31,621,183 series and 90.5% of the sequence count in one configuration, amounting to 279.724B observations [2409.16040].

- **Synthetic data (KernelSynth):**  
  Generated using mixtures of periodic and trend kernels to produce high-frequency, operationally realistic sequences, contributing ∼14.5% of total datapoints [2511.19841][2409.16040].

A strictly sliding-window approach is used: series are segmented into overlapping context-horizon windows (context length = 512 points per resolution; horizon length = 128 points) with stride length tuned to avoid domain or source dominance.

### Domain and Series Statistics

| Domain      | # Series      | # Observations (B) | % Obs |
|-------------|--------------|-------------------|-------|
| Nature      | 31,621,183   | 279.724           | 90.50 |
| Synthetic   | 11,968,625   | 9.222             | 2.98  |
| Energy      | 2,875,335    | 15.981            | 5.17  |
| Web         | 972,158      | 1.804             | 0.58  |
| Transport   | 622,414      | 2.130             | 0.69  |
| Sales       | 110,210      | 0.026             | 0.008 |
| Other       | 40,265       | 0.020             | 0.006 |
| Finance     | 1,715        | 0.0004            | 0.0001|
| Healthcare  | 1,752        | 0.0005            | 0.0001|

(*Data sourced from [2409.16040], Table 1. All numbers are approximate.*)

## 2. Data Domains, Modalities, and Source Taxonomy

Time-300B encompasses nine primary domains:

- **Observability:** Infrastructure metrics (CPU, memory, disk), application/service telemetry (request rates, latency), real-user and component metrics.
- **Energy:** Electricity load, building/power consumption, renewables.
- **Finance:** Exchange rates, stock indices, macroeconomic indicators.
- **Healthcare:** Hospital visits, epidemic time series, mortality.
- **Nature:** High-volume environmental data (weather, air quality, sunspots).
- **Sales:** Point-of-sale, retail, hierarchical aggregates, forecasting competition series.
- **Transport:** Traffic, mobility, public transit, ridesharing.
- **Web:** Web traffic, cluster resource traces, Wikipedia pageviews.
- **Synthetic:** Constructed via TS-Mixup, KernelSynth, and similar methods to model diverse periodic and stochastic phenomena.

Sampling frequencies range from sub-minute to annual, depending on domain (e.g., 4s, 15min, 1h, 1d, monthly, and synthetic, with no inherent physical time granularity).

## 3. Multiresolution Structure and Temporal Context

A defining characteristic is the multiresolution example design [2511.19841]:

- **Training examples:** Each consists of
  - Coarse-resolution context $x_c \in \mathbb{R}^{512}$ (e.g., 512 points of 1-hour aggregates).
  - Fine-resolution context $x_f \in \mathbb{R}^{512}$ (512 raw 1-minute points).
  - Fine-resolution horizon $y \in \mathbb{R}^{128}$ (128 future 1-minute points).
- **Resolution ratio:** $K=60$ (i.e., 60 fine-resolution points per coarse point).
- **Training regime:** Multiresolution transformer models fuse $x_c$ and $x_f$ for prediction; at inference, fine-resolution predictions are rolled up to update the coarse context via:
  $$
  \left[ \frac{1}{K} \sum_{j=1}^{K} \hat{y}_{(k-1)K+j} \right]_{k=1}^{\lfloor L/K \rfloor}
  $$
- Both 1-minute/1-hour and 5-minute/5-hour resolutions are included.

This design explicitly enables the learning of temporal dependencies across multiple scales, critical for operational and long-horizon forecasting [2511.19841].

## 4. Preprocessing, Filtering, and Normalization

The Time-300B preprocessing pipeline implements extensive per-series, per-window, and per-batch procedures [2511.19841][2409.16040]:

- **Series-level filtering:**  
  - Exclude series with excessive missing values or extended flat intervals.
  - Fill short gaps by last-value extrapolation.
  - Apply first-difference transform to cumulative counters to mitigate artificial growth.
- **Window-level filtering:**  
  - Reject windows with high unpredictability, quantified by the ratio:
    $$
    \frac{\max|\text{horizon deviation}|}{\max|\text{context deviation}|}
    $$
    above a fixed threshold.
  - Downsample windows with excessive spectral entropy.
  - Remove windows with too few unique values.
  - Adapt stride per dataset to avoid overrepresentation.
- **Deduplication (fine windows):**  
  - SimHash clustering and sampling to ensure diversity; histogram-based sampling for large clusters.
- **On-the-fly context normalization:**  
  - For each context $(x_c, x_f)$, calculate mean and std of the first 32 points.
  - Normalize:
    $$
    x_c \leftarrow \frac{x_c - \mu_c}{\sigma_c},\quad x_f \leftarrow \frac{x_f - \mu_f}{\sigma_f}
    $$
  - Model predicts normalized $y$; outputs are un-normalized at inference.
- **Synthetic augmentation:**  
  - Synthetic series from KernelSynth (Gaussian + periodic kernels), designed for high-frequency periodic structure.
  - No adversarial or noise-based augmentation beyond data selection and deduplication.

For the public Time-300B variant [2409.16040], preprocessing includes:

- Missing-value splitting: segmentation at $\mathrm{NaN}$/$\pm\infty$,
- Window-quality filtering: sliding window zero/first/second-difference flatness tests (thresholds at 0.2),
- Downsampling large domains,
- No explicit global normalization.

## 5. Data Splits, Batching, and Sampling Strategies

Partitioning and batching approaches are tailored for large-scale pretraining and robust evaluation [2511.19841][2409.16040]:

- **Splits (Cisco Time Series Model):**  
  - Sequence windows are sorted by timestamp, partitioned into strictly train, validation, and test sets (preventing temporal leakage).
  - Entire series are held out for zero-shot, out-of-domain testing.
  - Test windows originate from time intervals later than the latest in train/validation.
- **Splits (Time-MoE):**  
  - No explicit corpus-wide train/val/test splits for pretraining. During pretraining, batches are sampled to maintain balanced domain and value distribution. Only benchmark datasets (e.g., ETT, Global Temp) use classical splits for downstream evaluation.

- **Batching and packing:**  
  - Short sequences are concatenated (packed) to minimize padding.
  - Random cropping produces fixed-length subsequences per batch (max length 4,096).
  - Batches: 1,024 sequences per step (~4 million observations).
  - No MoE routing during data prep; routing occurs during model forward pass [2409.16040].

## 6. Storage, Format, and Access

Time-300B is distributed as sharded, binary-encoded records accompanied by detailed metadata [2511.19841][2409.16040]:

- **Object storage sharding:**  
  - Each shard contains serialized windows $(x_c, x_f, y)$ (Cisco) or subsequences (Time-MoE) and per-window metadata (e.g., series ID, source domain, resolution, cluster code, entropy score, timestamps).
  - Public Time-300B includes a metafile indexing by (dataset, start_offset, end_offset) to enable constant-memory streaming.
- **Data parallel training:**  
  - Shards are statically mapped to compute resources; normalization and filtering are applied on-the-fly by data workers.
- **Licensing and availability:**  
  - Public Time-300B consolidates datasets from UCI, Kaggle, Zenodo, and related sources, preserving upstream licensing. The unified dataset and loader utilities are open-source, released with documentation at https://github.com/Time-MoE/Time-MoE [2409.16040].

## 7. Significance and Applications

Time-300B represents a foundational advance in time series data infrastructure for large model pretraining [2511.19841][2409.16040]:

- Its multi-domain, multiresolution pipeline supports both general-purpose and observability-specialized foundation models.
- The dataset demonstrates the practical application of scaling laws (tokens, model size) for time series, paralleling advances in NLP and vision.
- Time-300B supports univariate, zero-shot, and cross-domain forecasting without per-series fine-tuning, enabling benchmarking for both dense and Mixture-of-Experts (MoE) architectures.
- The inclusion of both proprietary and open-access variants enables reproducibility, benchmarking, and further research using modern GPU-scale learning pipelines.

The design principles of Time-300B—scale, diversity, multiresolution, and balance—emerged as critical properties for enabling the next generation of zero-shot time series forecasting and efficient, cross-domain transfer [2511.19841][2409.16040].

Source: https://www.emergentmind.com/topics/time-300b-dataset