# Send time-sereis-analysis to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "time-series-analysis", "name": "time-sereis-analysis", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/dubnium0/time-series-analysis", "canonicalUrl": "https://clawhub.ai/dubnium0/time-series-analysis", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/time-series-analysis", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=time-series-analysis", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILLS.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "time-series-analysis", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T17:17:12.043Z", "expiresAt": "2026-05-07T17:17:12.043Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=time-series-analysis", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=time-series-analysis", "contentDisposition": "attachment; filename=\"time-series-analysis-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "time-series-analysis" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/time-series-analysis" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/time-series-analysis", "downloadUrl": "https://openagent3.xyz/downloads/time-series-analysis", "agentUrl": "https://openagent3.xyz/skills/time-series-analysis/agent", "manifestUrl": "https://openagent3.xyz/skills/time-series-analysis/agent.json", "briefUrl": "https://openagent3.xyz/skills/time-series-analysis/agent.md" } } ``` ## 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 ## Trust - Source: tencent - Verification: Indexed source record - Publisher: dubnium0 - Version: 1.0.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-04-30T17:17:12.043Z - Expires at: 2026-05-07T17:17:12.043Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/time-series-analysis) - [Send to Agent page](https://openagent3.xyz/skills/time-series-analysis/agent) - [JSON manifest](https://openagent3.xyz/skills/time-series-analysis/agent.json) - [Markdown brief](https://openagent3.xyz/skills/time-series-analysis/agent.md) - [Download page](https://openagent3.xyz/downloads/time-series-analysis)