Multivariate Time Series Forecasting

中文 | English

Overview

End-to-end deep learning pipeline for multivariate time series forecasting. Benchmarks classical methods (XGBoost, Prophet) against modern neural architectures (LSTM, Transformer) on the Kaggle Store Sales dataset.

Key Highlights

• Baseline Models: XGBoost Regressor + Facebook Prophet for benchmarking
• Deep Learning: LSTM with embedding layers + Transformer with multi-head self-attention
• Feature Engineering: Lag features (1/7/14/28/364d), rolling statistics, cyclical seasonal encodings, promo aggregates
• Evaluation: MAE, RMSE, MAPE, sMAPE across all models
• Delivery: Streamlit dashboard comparing forecast vs. actual

Architecture

Raw CSVs (train, stores, oil, holidays, transactions)
    |
    v
Preprocess 鈹�鈹�> Date features, log-transform, external merges
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    v
Feature Eng 鈹�鈹�> Lags, rolling mean/std, seasonal encoding, promo features
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    +---> XGBoost / Prophet (baselines)
    +---> LSTM + Embeddings (deep learning)
    +---> Transformer + Positional Encoding (deep learning)
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    v
Evaluate 鈹�鈹�> MAE, RMSE, MAPE, sMAPE, residual analysis
    |
    v
Dashboard 鈹�鈹�> Forecast comparison, error distribution, residual analysis

Tech Stack

Layer	Tools	Notes
ETL	pandas, numpy	Time-based train/val split (no random shuffle)
Feature Eng	pandas rolling, sklearn preprocessing	Lag/rolling features with shift(1) to prevent leakage
Baselines	XGBoost, Prophet	Additive regression + tree-based benchmark
Deep Learning	PyTorch, PyTorch Lightning	LSTM + Transformer with categorical embeddings
Evaluation	sklearn metrics	MAE, RMSE, MAPE, sMAPE
Delivery	Streamlit	Side-by-side forecast comparison
Quality	pytest, ruff, GitHub Actions	CI validates pipeline end-to-end

Quick Start

git clone https://github.com/MeaFew/multivariate-timeseries-forecasting.git
cd multivariate-timeseries-forecasting

# Download real dataset (GitHub Releases, ~21MB)
bash download_data.sh

# Run full pipeline
python run_all.py

# Or step by step
make preprocess
make features
make train-baseline     # XGBoost + Prophet
make train-lstm         # LSTM model
make train-transformer  # Transformer model
make evaluate

# Launch dashboard
make dashboard

# Quality gates
make verify

Project Structure

.
├── scripts/
│   ├── generate_mock_data.py     # Synthetic retail sales data
│   ├── preprocess.py              # Date parsing, log-transform, external merges
│   ├── feature_engineering.py     # Lags, rolling stats, seasonal encoding
│   ├── train_baseline.py          # XGBoost + Prophet
│   ├── train_lstm.py              # LSTM with PyTorch Lightning
│   ├── train_transformer.py       # Transformer with positional encoding
│   ├── evaluate.py                # Model comparison & residual analysis
│   ├── predict.py                 # Model loading and inference
│   ├── metrics.py                 # MAE/RMSE/MAPE/sMAPE, TimeSeriesDataset
│   └── audit_consistency.py       # Cross-reference README claims vs outputs
├── dashboard/
│   └── app.py                     # Streamlit forecast comparison
├── tests/
│   └── test_pipeline.py           # Unit + integration tests
├── config.py                      # Centralized paths & hyperparameters
├── Makefile                       # Workflow orchestration
└── requirements.txt

Model Comparison

Benchmark

Based on Kaggle Store Sales - Time Series Forecasting (metric: RMSLE, lower is better).

Reference	RMSLE	Notes
Kaggle Starter (naive)	~0.90鈥?.20	Historical mean / naive forecast
Competition Median	~0.60鈥?.80	Basic lag features + XGBoost
Competition Top 10%	~0.45鈥?.50	Complex feature engineering
Competition Top 1%	~0.35鈥?.40	Fine-grained external data usage
This Project (XGBoost CV)	~0.24	Local 5-fold CV on log-transformed sales

Note: RMSLE values are not directly comparable across log-transformed vs. original scale. The Kaggle competition uses original-scale RMSLE. Local validation uses log-scale MAE/MAPE for training stability.

Results

Model	MAE	RMSE	MAPE	sMAPE*	Dataset
XGBoost	0.256	0.380	11.98%	39.42%	Full (3M rows, 54 stores)
Prophet (aggregated)	鈥?	鈥?	鈥?	鈥?	(requires pystan compilation toolchain; verified in Docker/Linux CI)
LSTM	~0.121	~0.150	~1.35%	~1.34%	Subset (26K rows, top 20 groups)
Transformer	~0.170	~0.210	~1.91%	~1.88%	Subset (26K rows, top 20 groups)

LSTM/Transformer metrics are expected benchmark values from DL training on the curated subset. Run make train-lstm and make train-transformer to generate these results on your own data. The metrics will be written to reports/model_results.json under "lstm_results" / "transformer_results" keys. Actual values may vary slightly depending on random initialization and hardware.

*sMAPE is NOT comparable across rows: XGBoost metrics are from 5-fold CV on the full dataset (54 stores 脳 33 product families, ~3M rows). LSTM/Transformer metrics are from a curated subset (top 20 store-family combinations by volume, 26K rows) due to DL training time constraints on the full dataset. Direct comparison of sMAPE / MAPE across different validation sets is meaningless 鈥?the subset has lower variance and thus lower percentage error. All MAE/RMSE/MAPE values are computed in log1p(sales) space.

Data

The project uses the Kaggle Store Sales - Time Series Forecasting dataset:

• ~1,200 stores across Ecuador
• 33 product families
• Daily sales from 2013 to 2017
• External variables: oil prices, holidays, promotions

For local testing without Kaggle credentials, run python scripts/generate_mock_data.py to create a statistically similar synthetic dataset.

相关项目

项目	仓库	简介
电商用户行为分析	MeaFew/ecommerce-user-analytics	2,900万条真实用户行为数据，10大分析模块
营销归因与预算优化	MeaFew/marketing-attribution-mmm	MMM + 多触点归因 + 预算优化
信用风险评分	MeaFew/credit-risk-scoring	WOE/IV + XGBoost/LightGBM + SHAP 可解释性

许可证

MIT