Time Series Forecasting
Transformers
PyTorch
Korean
jnu_tsb
feature-extraction
jnu-tsb
time-series
forecasting
chronos-2
polyglot-ko
korean
finance
covariates
r
reticulate
education
custom_code
Instructions to use HONGRIZON/JNU-TSB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HONGRIZON/JNU-TSB with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HONGRIZON/JNU-TSB", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,536 Bytes
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"model_type": "jnu_tsb",
"architectures": [
"JNUTSBModel"
],
"repo_id": "HONGRIZON/JNU-TSB",
"project_name": "JNU-TSB",
"project_full_name": "Jeju National University Time-Series Bridge",
"description": "한국어 뉴스 기반 공변량 추출과 Chronos-2 시계열 예측을 연결하는 교육용 Time-LLM-style router.",
"chronos_model_id": "amazon/chronos-2",
"llm_model_id": "EleutherAI/polyglot-ko-1.3b",
"timestamp_column": "timestamp",
"target_column": "target",
"id_column": "item_id",
"default_item_id": "series_0",
"prediction_length": 5,
"quantile_levels": [
0.1,
0.5,
0.9
],
"event_categories": [
"earnings",
"product",
"macro",
"regulation",
"supply_chain",
"competition",
"other"
],
"covariate_columns": [
"cov_earnings_count",
"cov_product_count",
"cov_macro_count",
"cov_regulation_count",
"cov_supply_chain_count",
"cov_competition_count",
"cov_other_count",
"cov_sentiment_pos_count",
"cov_sentiment_neg_count",
"cov_sentiment_neu_count",
"cov_news_count",
"cov_sentiment_mean",
"cov_confidence_mean",
"cov_event_score"
],
"use_llm_extractor": true,
"allow_naive_fallback": true,
"auto_map": {
"AutoConfig": "configuration_jnu_tsb.JNUTSBConfig",
"AutoModel": "modeling_jnu_tsb.JNUTSBModel"
},
"custom_pipelines": {
"jnu-tsb": {
"impl": "pipeline.JNUTSBPipeline",
"pt": [
"AutoModel"
],
"type": "multimodal"
}
}
}
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