---
license: apache-2.0
tags:
- timeseries
configs:
- config_name: default
  data_files:
  - split: gift_eval_256
    path: gift-eval_256/*.arrow
  - split: gift_eval_512
    path: gift-eval_512/*.arrow
  - split: electricity_256
    path: electricity_256/*.arrow
  - split: electricity_512
    path: electricity_512/*.arrow
  - split: ett1_15T_256
    path: ett1-15T_256/*.arrow
  - split: ett1_15T_512
    path: ett1-15T_512/*.arrow
  - split: rule_dataset_512
    path: rule_dataset_512/*.arrow
dataset_info:
  features:
  - name: target
    sequence: float64
  - name: dataset
    dtype: string
  - name: seq_length
    dtype: int64
  splits:
  - name: gift_eval_256
    num_examples: 11000
  - name: gift_eval_512
    num_examples: 11000
  - name: electricity_256
    num_examples: 10000
  - name: electricity_512
    num_examples: 10000
  - name: ett1_15T_256
    num_examples: 7000
  - name: ett1_15T_512
    num_examples: 7000
  - name: rule_dataset_512
    num_examples: 11000
---

# Time-TSNE-Plot

A multi-scenario evaluation collection for t-SNE visualization of time-series embeddings. Each split stores samples with a fixed window length; the `dataset` field carries **different label semantics** depending on the split (see below).

## Evaluation Dimensions

| Dimension | Description | Recommended splits |
|-----------|-------------|-------------------|
| **1. Embeddings across datasets** | Multiple real/benchmark datasets; labels are dataset names | `gift_eval_256` / `gift_eval_512` |
| **2. Embeddings across rule-based series** | Rule generators such as triangle waves, square waves, ARMA, etc. | `rule_dataset_512` |
| **3. Embeddings across channels (same dataset)** | Multivariate ETT1 with sliding windows per channel; labels are channel indices | `ett1_15T_256` / `ett1_15T_512` |
| **4. Embeddings across time positions (same channel)** | Univariate Electricity with sliding windows; labels are window start timestamp indices | `electricity_256` / `electricity_512` |

## Splits Overview

### GIFT-Eval (labeled by dataset name)

Sampled from GIFT-Eval subsets; `dataset` is the **source dataset name** (e.g. `bitbrains_fast_storage`, `m4`, etc.; 11 categories in total).

| Split | Directory | # Samples | Window length |
|-------|-----------|-----------|---------------|
| `gift_eval_256` | `gift-eval_256/` | 11,000 | 256 |
| `gift_eval_512` | `gift-eval_512/` | 11,000 | 512 |

### Electricity (labeled by timestamp)

**Sliding-window** segments on a univariate Electricity series; `dataset` is the **start timestamp index** of each window (string, e.g. `"0"`, `"1"`, …).

| Split | Directory | # Samples | Window length |
|-------|-----------|-----------|---------------|
| `electricity_256` | `electricity_256/` | 10,000 | 256 |
| `electricity_512` | `electricity_512/` | 10,000 | 512 |

### ETT1-15T (labeled by channel)

**Sliding-window** segments on multivariate ETT1 (15-minute) series; `dataset` is the **channel index** (`"0"`–`"6"`, 7 channels in total).

| Split | Directory | # Samples | Window length |
|-------|-----------|-----------|---------------|
| `ett1_15T_256` | `ett1-15T_256/` | 7,000 | 256 |
| `ett1_15T_512` | `ett1-15T_512/` | 7,000 | 512 |

### Rule (labeled by generator)

Synthetic series from rule-based generators (e.g. `generate_triangle_wave`, `generate_square_wave`, `generate_arma_samples`, etc.; 10 categories); `dataset` is the **rule/generator name**. Only window length 512 is provided for now.

| Split | Directory | # Samples | Window length |
|-------|-----------|-----------|---------------|
| `rule_dataset_512` | `rule_dataset_512/` | 11,000 | 512 |

## Field Descriptions

| Field | Type | Meaning |
|-------|------|---------|
| `target` | `list[float64]` | Time-series values within the window |
| `dataset` | `string` | **Label** (semantics vary by split; see above) |
| `seq_length` | `int64` | Window length (256 or 512) |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("whenxuan/Time-TSNE-Plot")

# 1) Cross-dataset embeddings
gift = ds["gift_eval_256"]

# 2) Rule-based series embeddings
rules = ds["rule_dataset_512"]

# 3) Different channels within the same dataset
ett = ds["ett1_15T_256"]

# 4) Different time positions within the same channel
elec = ds["electricity_256"]
```