whenxuan: update new datasets

README.md

@@ -9,6 +9,16 @@ configs:
     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
@@ -22,18 +32,75 @@ dataset_info:
     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
 
-Time-series subsets for t-SNE visualization, with two splits by context length.
+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 |
 
-## Splits
+### ETT1-15T (labeled by channel)
 
-| Split | Rows | Description |
-|-------|------|-------------|
-| `gift_eval_256` | 11,000 | Context length 256 (`gift-eval_256/`) |
-| `gift_eval_512` | 11,000 | Context length 512 (`gift-eval_512/`) |
+**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
 
@@ -41,6 +108,16 @@ Time-series subsets for t-SNE visualization, with two splits by context length.
 from datasets import load_dataset
 
 ds = load_dataset("whenxuan/Time-TSNE-Plot")
-ds_256 = ds["gift_eval_256"]
-ds_512 = ds["gift_eval_512"]
+
+# 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"]
 ```

electricity_256/data-00000-of-00001.arrow

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electricity_256/dataset_info.json

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