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- OpenClaw
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- Extract archive
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- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
time-sereis-analysis
Comprehensive time series data science skill covering feature engineering, model training, and competition-winning strategies for forecasting and prediction problems.
skill openclawclawhub Free
Comprehensive time series data science skill covering feature engineering, model training, and competition-winning strategies for forecasting and prediction problems.
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Release facts
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- Tencent SkillHub
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- Version
- 1.0.0
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- dubnium0
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Documentation
Time Series Data Science - Complete Guide
Expert time series data scientist specializing in forecasting, sequential prediction, and competition-winning strategies. This skill covers the complete pipeline from EDA to production-ready models.
Key Lessons from Winning Solutions
Feature Engineering > Model Complexity Focus on 5-10 most predictive features, not all available Lag, rolling, and EWM features are often more valuable than the raw data Interaction features between top predictors can be game-changers Time-Based Validation is Critical NEVER use random splits for time series Train on past, validate on future (e.g., ts_index <= threshold) Leakage from future data will destroy real-world performance Weights Matter in Scoring If weights are provided, use them directly in training High-weight samples disproportionately affect score Sample weighting in model.fit() is better than custom loss Multi-Seed Ensemble for Robustness Train same model with different random seeds Average predictions reduces variance Common seeds: 42, 2024, or any fixed set
1. Lag Features
GROUP_COLS = ['entity_id', 'category', 'horizon'] for lag in [1, 3, 5, 10]: df[f'{col}_lag{lag}'] = df.groupby(GROUP_COLS)[col].shift(lag)
2. Rolling Statistics
for window in [5, 10, 20]: df[f'{col}_roll_mean{window}'] = df.groupby(GROUP_COLS)[col].transform( lambda x: x.rolling(window, min_periods=1).mean() ) df[f'{col}_roll_std{window}'] = df.groupby(GROUP_COLS)[col].transform( lambda x: x.rolling(window, min_periods=1).std() )
3. Exponential Weighted Mean (EWM)
for span in [5, 10]: df[f'{col}_ewm{span}'] = df.groupby(GROUP_COLS)[col].transform( lambda x: x.ewm(span=span, adjust=False).mean() )
4. Difference Features
df[f'{col}_diff1'] = df.groupby(GROUP_COLS)[col].diff(1) df[f'{col}_diff_pct'] = df.groupby(GROUP_COLS)[col].pct_change(1)
5. Interaction Features
# Difference between related features df['feat_diff'] = df['feature_a'] - df['feature_b'] # Ratio between features df['feat_ratio'] = df['feature_a'] / (df['feature_b'] + 1e-7) # Product interactions df['feat_product'] = df['feature_a'] * df['feature_b']
6. Target Encoding (for categories)
# Compute on training data only (ts_index <= threshold) train_only = df[df.ts_index <= VAL_THRESHOLD] enc_stats = { 'category': train_only.groupby('category')['target'].mean().to_dict(), 'global_mean': train_only['target'].mean() } # Apply to all data df['category_enc'] = df['category'].map(enc_stats['category']).fillna(enc_stats['global_mean'])
7. Temporal Signals
# Cyclical encoding for periodicity df['t_cycle'] = np.sin(2 * np.pi * df['ts_index'] / period) df['t_cycle_cos'] = np.cos(2 * np.pi * df['ts_index'] / period) # Normalized time position df['ts_normalized'] = df['ts_index'] / df['ts_index'].max() # Time bins df['ts_bin'] = pd.cut(df['ts_index'], bins=10, labels=False)
LightGBM Configuration (Competition-Tested)
lgb_cfg = { 'objective': 'regression', 'metric': 'rmse', 'learning_rate': 0.015, 'n_estimators': 4000, 'num_leaves': 80, 'min_child_samples': 200, 'feature_fraction': 0.6, 'bagging_fraction': 0.7, 'bagging_freq': 5, 'lambda_l1': 0.1, 'lambda_l2': 10.0, 'verbosity': -1 }
Multi-Seed Ensemble Training
val_pred = np.zeros(len(y_val)) test_pred = np.zeros(len(X_test)) for seed in [42, 2024]: model = lgb.LGBMRegressor(**lgb_cfg, random_state=seed) model.fit( X_train, y_train, sample_weight=w_train, # Use weights directly eval_set=[(X_val, y_val)], eval_sample_weight=[w_val], callbacks=[lgb.early_stopping(200, verbose=False)] ) val_pred += model.predict(X_val) / 2 test_pred += model.predict(X_test) / 2
Horizon-Specific Models
# Train separate model per forecast horizon for horizon in [1, 3, 10, 25]: train_h = df[df.horizon == horizon] test_h = test_df[test_df.horizon == horizon] # Build features, train model model = train_model(train_h, test_h) predictions[horizon] = model.predict(test_h)
Time-Based Split
VAL_THRESHOLD = int(df['ts_index'].max() * 0.85) train_mask = df['ts_index'] <= VAL_THRESHOLD val_mask = df['ts_index'] > VAL_THRESHOLD X_train = df.loc[train_mask, feature_cols] X_val = df.loc[val_mask, feature_cols]
Expanding Window Cross-Validation
from sklearn.model_selection import TimeSeriesSplit tscv = TimeSeriesSplit(n_splits=5) for train_idx, val_idx in tscv.split(df): # Train on expanding window pass
Custom Metrics
def weighted_rmse_score(y_true, y_pred, weights): """Weighted RMSE skill score (higher is better)""" denom = np.sum(weights * y_true**2) if denom <= 0: return 0.0 numer = np.sum(weights * (y_true - y_pred)**2) ratio = numer / denom return float(np.sqrt(1.0 - np.clip(ratio, 0.0, 1.0)))
EDA Checklist
Target Analysis Distribution by time period Distribution by category/horizon Trend and seasonality detection Missing Values Pattern analysis (random vs systematic) Group-based imputation strategy Weight Distribution Concentration analysis Impact on scoring metric Feature Correlations Correlation with target Multicollinearity between features Temporal Patterns Stationarity tests Rolling statistics visualization
Common Pitfalls to Avoid
PitfallSolutionRandom train/test splitUse time-based splitUsing future data for encodingCompute stats on train onlyIgnoring sample weightsUse sample_weight in fit()Too many featuresFocus on top 5-10 predictorsSingle modelMulti-seed ensembleOverfitting validationLarge early stopping patience
Competition Workflow
graph TD A[Load Data] --> B[Compute Encoding Stats on Train] B --> C[Build Features] C --> D[Time-Based Split] D --> E{For Each Horizon} E --> F[Train Multi-Seed Ensemble] F --> G[Validate & Score] G --> H[Generate Predictions] H --> I[Aggregate & Submit]
Quick Reference Commands
# Run complete pipeline python train_winning.py # Generate submission python generate_submission.py # Validate submission format python -c " import pandas as pd sub = pd.read_csv('submission.csv') print(f'Rows: {len(sub)}, Cols: {list(sub.columns)}') print(sub.head()) "
Integration with Other Workflows
Use with /data-analyst for comprehensive EDA Use with /data-scientist for advanced feature engineering Use with /fintech-engineer for financial risk analysis Combine predictions with /quant-analyst for portfolio strategies
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILLS.md