albertooooz/chronos-2-lora-pl-day-ahead

Chronos-2 LoRA — Poland day-ahead electricity prices

LoRA adapter for 24-hour-ahead hourly day-ahead (DA) electricity price forecasting in Poland (ENTSO-E bidding zone PL). Fine-tuned on top of amazon/chronos-2 using ENTSO-E price history. Weather covariates were not used during training but can be supplied at inference.

Intended use

• Univariate DA price forecasting for the Polish bidding zone (PL)
• Load with the Chronos-2 pipeline (PEFT LoRA adapter, not a standalone full model)
• Optional Open-Meteo weather covariates at inference via predict_df
• Research and prototyping — not financial or trading advice

Usage

Install dependencies:

pip install chronos-forecasting peft torch

Load the adapter (Chronos merges the LoRA weights automatically):

from chronos import Chronos2Pipeline
import numpy as np

pipeline = Chronos2Pipeline.from_pretrained("albertooooz/chronos-2-lora-pl-day-ahead")

# Univariate context: shape (n_series, n_variates, history_length)
context = np.array(your_hourly_prices, dtype=np.float64).reshape(1, 1, -1)
forecast = pipeline.predict(inputs=context, prediction_length=24)

Training data

Field	Value
Source	ENTSO-E Transparency Platform (day-ahead prices)
Zone	PL
Frequency	1h (resampled from 15min where needed)
Train rows	85,523
Date range	2015-01-04 → 2025-07-31
Holdout	Last 12 months excluded from training (benchmark eval window)
Weather at train	none

Training hyperparameters

Parameter	Value
Fine-tuning mode	lora
LoRA rank (r)	8
LoRA alpha	16
Learning rate	1e-05
Steps	1000
Batch size	64
Context length	2048
Prediction length	24
Hardware	RunPod CUDA (PyTorch 2.4.1+cu124)

Target modules: self_attention.q/k/v/o, output_patch_embedding.output_layer.

Evaluation

Walk-forward backtest on the held-out 12 months: 2 random origin days per calendar month, horizon 24h, random_seed=42, 1h frequency — same protocol as benchmark v1.

Model	Covariates	Zeroshot MASE	LoRA MASE	Δ MASE
chronos-2-small	actual_weather	0.814	0.609	+25.1%
chronos-2-small	none	0.843	0.632	+25.0%
chronos-2	actual_weather	0.779	0.578	+25.8%
chronos-2	none	0.824	0.614	+25.5%

Metrics: MASE (primary), MAE. MASE scale uses in-sample seasonal naive on context up to each origin.

See eval_results.json in this repository for structured metrics.

Limitations

• Trained only on Polish (PL) day-ahead prices; do not expect strong transfer to other zones or markets without retraining
• Does not model fundamental drivers (fuel, outages, cross-border flows) beyond price history (and optional weather at inference)
• Outputs are probabilistic point/quantile forecasts — validate before production use
• Not a substitute for professional energy market forecasting or risk management

Base model

This is a LoRA adapter — load it together with amazon/chronos-2. If you use this adapter in research, cite the Chronos-2 foundation model.

Model card contact

albertooooz