Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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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

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"]

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