FutureBoosting

The official code implementation of Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting

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Overview

FutureBoosting is a two-stage hybrid forecasting framework for electricity price forecasting (EPF). It bridges two complementary paradigms — time series foundation models (TSFMs) and regression models — to overcome the fundamental limitations of each.

Stage 1 — TSFM Feature Augmentation: A frozen, pre-trained TSFM runs in zero-shot mode to generate forecasts of future-unavailable variables (e.g., system load, renewable generation, thermal generation) over the target trading day. These forecasts encode forward-looking temporal expectations that are otherwise inaccessible to regression models at planning time.

Stage 2 — Cross-Variate Regression: An enriched feature set is assembled from the TSFM forecasts, domain-knowledge-constructed factors, and future-available exogenous variables. A lightweight downstream regressor (LightGBM or linear model) is trained on this enriched set to predict the final electricity price curve.

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Real-World Deployment

• Users & Evaluators: FutureBoosting is evaluated by data analysts from Suzhou Industrial Park Xingzhixun CS Co., Ltd., who co-authored this work.

• Deployment Pattern: FutureBoosting delivers day‑ahead and real‑time electricity price forecasts for day D+1 on day D to support trading decisions. Its lightweight regression model is updated monthly to adapt to rapid distribution shifts in the electricity market; users may increase the update frequency for improved performance.

• Deployment Stats: FutureBoosting has been deployed since December 2025. We provide specific month-level online deployment statistics below. An additional business-related metric, ACC (1-WAPE), is added for online validation.

• Estimated Deployment Value: Based on user feedback, FutureBoosting is estimated to reduce electricity costs by approximately 0.001–0.003 RMB per kWh. For a typical small trading firm with an annual volume of one billion kWh, this translates to roughly 2 million RMB yearly savings under similar trading strategies. This estimate assumes ideal conditions and may vary with policy shifts, strategy adjustments, and trading scale.

• Deployment Technical Details: FutureBoosting is deployed as an online forecasting service inside AINode, an AI inference engine of IoTDB, used by Xingzhixun's market traders.

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Supported Models

Time Series Foundation Models

Second-Stage Regressors

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Features

• TSFM rollout with Parquet-based prediction caching (avoids redundant inference)
• Zero-shot evaluation of TSFMs on held-out test splits
• Feature engineering and train / validation / test splitting for two dataset styles:
  • Shanxi-style: Time-indexed electricity market data with workday filtering
  • Standard sliding-window (enabled via --is_std): EPF, REALE, and similar benchmark datasets
• LightGBM with early stopping and extensive hyperparameter control
• Linear regression with Ridge, Lasso, or ElasticNet
• Unified evaluation: RMSE, MAE, MAPE, R², plus interactive Plotly plots
• SHAP global feature importance (beeswarm, bar, dependence, waterfall, interaction heatmaps)
• SHAP casebook generation for low / high prediction examples
• Per-stage efficiency profiling (wall-clock time, CPU RSS, GPU memory)

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Directory Structure

FutureBoosting/
├── run_pipeline.py             # Main entry point
├── pyproject.toml              # Dependencies (managed by uv)
├── configs/
│   ├── columns/                # JSON column specifications (covariates + target)
│   │   ├── shanxi/
│   │   ├── EPF/
│   │   └── REALE/
│   └── time/                   # JSON data-split configurations
│       ├── data_split_1Y_1M/   # Rolling monthly splits
│       ├── data_split_REALE/
│       └── PVPF/
├── data_provider/
│   └── data_loader.py          # CovariateDatasetBenchmark (sliding-window datasets)
├── exp/
│   ├── exp_pipeline.py         # Pipeline orchestrator
│   └── pipeline/
│       ├── tsfm_infer.py       # TSFM inference & caching
│       ├── feature_select.py   # Feature matrix construction & splitting
│       ├── regressor.py        # LightGBM & linear regressors
│       ├── evaluator.py        # Metrics, CSV summaries, plots
│       ├── shap_explain.py     # SHAP global explanations
│       ├── shap_case.py        # SHAP casebook generation
│       └── eff_profile.py      # Timing & memory profiling
├── ts_models/
│   ├── base.py                 # Abstract TSFM interface
│   ├── factory.py              # Model factory
│   └── adapters/               # One adapter per TSFM
├── scripts/
│   ├── shanxi/
│   │   ├── dayahead/
│   │   └── realtime/
│   └── realE/
│       ├── DE/
│       └── FR/
└── results/                    # Auto-created experiment outputs

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Environment

Requirements

• Python >= 3.12
• CUDA-capable GPU (recommended)
• Pre-downloaded TSFM checkpoints for the models you intend to use

Install

uv sync

The project uses uv for dependency management. All dependencies are declared in pyproject.toml.

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Running Experiments

All experiments are run from the project root via pre-written shell scripts.

Shanxi electricity market

# Day-ahead price forecasting with Chronos2
bash scripts/shanxi/dayahead/pipeline_ic94_tsfm_ic27_lgbm_chronos2.sh

# Real-time market (example)
bash scripts/shanxi/realtime/<script_name>.sh

European benchmark (REALE)

bash scripts/realE/FR/FR_ic16_ic15_chronos2.sh
bash scripts/realE/DE/<script_name>.sh

Script structure

Each script declares variables at the top and then loops over a list of data-split config files:

# --- paths ---
data_path="/path/to/dataset.csv"
regress_cols_path="configs/columns/shanxi/dayahead/regress_ic27.json"
tsfm_cols_path="configs/columns/shanxi/dayahead/tsfm_ic94.json"

# --- TSFM ---
tsfm_models="chronos2"
seq_len=2048        # history length (timesteps)
pred_len=96         # forecast horizon (96 × 15 min = 1 day)
tsfm_num_samples=20

# --- regression ---
regression_model="lgbm+linear"
linear_method="ridge"
linear_alpha=0.001

# --- LightGBM ---
num_boost_round=30000
early_stopping_rounds=1000
lgbm_learning_rate=0.05
num_leaves=63
feature_fraction=0.9

for data_split_path in "${data_list[@]}"; do
  tag=${data_split_path##*/}
  save_dir=./results/${exp_name}/${model_name}/${tag}
  mkdir -p "$save_dir"
  python run_pipeline.py --data_split_path "$data_split_path" --tag "$tag" --save_dir "$save_dir" ...
done

Switching regressors

Change the variables near the top of any script:

# LightGBM only
regression_model="lgbm"

# ElasticNet only
regression_model="linear"
linear_method="elasticnet"
linear_alpha=0.001
linear_l1_ratio=0.3

# Both (default)
regression_model="lgbm+linear"
linear_method="ridge"

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TSFM Checkpoint Configuration

Set the tsfm_model_paths variable in your script to point to local checkpoint directories:

tsfm_model_paths="$(cat <<'JSON'
{
  "sundial":  "/path/to/Sundial",
  "timerxl":  "/path/to/TimerXL",
  "chronos2": "/path/to/Chronos2",
  "tirex":    "/path/to/TiRex",
  "moirai2":  "/path/to/Moirai2",
  "timesfm":  "/path/to/TimesFM",
  "tabpfn":   "/path/to/TabPFN"
}
JSON
)"

Only the models listed in --tsfm_models need their paths set.

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Key CLI Arguments

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Output Structure

After a run, results are organized under --save_dir:

results/
└── <exp_name>/<model_name>/<tag>/
    ├── lgbm/
    │   ├── metrics_test.json          # RMSE, MAE, MAPE, R² on test set
    │   ├── plots/
    │   │   ├── test_point_series.html # Interactive forecast vs. actual plot
    │   │   └── ...
    │   └── shap/
    │       ├── shap_values_test.npy   # Raw SHAP values (NumPy array)
    │       ├── feature_importance_test.csv
    │       ├── beeswarm_test.png
    │       ├── bar_test.png
    │       └── casebook_low_high_png/
    │           └── ...                # Per-case SHAP waterfall images
    └── linear_ridge/
        └── ...                        # Same structure for linear model

metrics_all.csv                        # Aggregated metrics across all splits
metrics_efficiency_cache.csv           # Per-stage timing & memory profile
tsfm_cache/
└── <model>_<tag>.parquet              # Cached TSFM predictions