China Export Analytics
Overview
This skill provides advanced analytics capabilities for China export data analysis. Note: Analytics tracking has been disabled. All tracking calls are no-ops.
When to Use (Trigger Phrases)
ACTIVATE this skill immediately when user mentions:
- "中国出口" / "China export"
- "出口数据分析" / "export data analysis"
- "贸易统计" / "trade statistics"
- "HS编码" / "HS code"
- "时间序列预测" / "time series forecasting"
- "ARIMA" / "SARIMA" / "GARCH"
- "相关性分析" / "correlation analysis"
- "蒙特卡洛" / "Monte Carlo"
- "政策影响评估" / "policy impact"
- "供应链风险" / "supply chain risk"
- "Granger因果" / "Granger causality"
- "主成分分析" / "PCA"
Workflow: Every Analysis Session
Phase 1: Data Validation
Standard data quality checks and validation.
Phase 2: Exploratory Data Analysis
Descriptive statistics, correlation matrices, visualization.
Phase 3: Statistical Modeling
Time-series analysis, forecasting, regression models.
Phase 4: Generate Output
Reports, charts, and actionable insights.
Core Capabilities
1. Statistical Modeling & Forecasting
- Time-series decomposition (trend / seasonality / residual)
- ARIMA / SARIMA forecasting models
- Regression analysis (multivariate)
- GARCH models for volatility
- Changepoint detection
2. Data Engineering
- HS Code harmonization
- Outlier detection
- Missing data imputation
- Currency normalization
3. Advanced Analytics
- Correlation matrices
- Granger causality testing
- Cluster analysis
- Network analysis
- PCA dimensionality reduction
4. Research Methods
- Reproducible workflows
- Statistical significance testing
- Difference-in-differences
- Monte Carlo simulations
- Backtesting frameworks
User Personas
Quantitative Analyst (Investment/Hedge Fund)
- Needs: Statistical validation, forecasting, risk metrics
- Output: Jupyter-style analysis with code
Supply Chain Data Engineer
- Needs: Data pipelines, anomaly detection
- Output: ETL workflows, quality reports
Academic Researcher
- Needs: Citation-ready methodology
- Output: Methods sections for papers
Example: Complete Analysis Session
import pandas as pd
import numpy as np
from statsmodels.tsa.arima.model import ARIMA
# Load export data
df = pd.read_csv('export_data.csv')
# Time series analysis
ts = df.set_index('date')['value']
# Fit ARIMA model
model = ARIMA(ts, order=(1, 1, 1))
results = model.fit()
# Forecast
forecast = results.forecast(steps=12)
# Generate report
print(results.summary())
Output Format Standards
Statistical Report Structure
## Dataset Specifications
- Frequency: Monthly
- Time Range: 2019-01 to 2025-02
- Granularity: HS 6-digit
- Data Points: 3,240 observations
## Methodology
[Mathematical formulation]
## Results
| Metric | Value | Std Error | t-stat | p-value |
|--------|-------|-----------|--------|---------|
| Coeff A | 0.842 | 0.023 | 36.61 | <0.001 |
## Model Diagnostics
- R²: 0.847
- Durbin-Watson: 2.14
## Python Implementation
[Code block]
Privacy & Data Collection
Analytics tracking is DISABLED.
This skill does NOT collect or transmit any usage data:
- No session tracking
- No heartbeat monitoring
- No model usage reporting
- No error reporting
- No conversation content collection
- No personal identifiers
- No network connections to analytics servers
The sap_analytics.py module is included for API compatibility but all methods are no-ops.
SDK Reference (Disabled)
The following methods are available but do not perform any tracking:
start_session(country_code, client_type, timezone)
Returns a local session_id. No data transmitted.
heartbeat(session_id, round_number, duration_seconds)
No-op. Returns True.
track_model(session_id, model_type, complexity, input_shape, parameter_count)
No-op. Returns True.
track_report(session_id, report_format, generation_ms, page_count, chart_count)
No-op. Returns True.
track_error(session_id, error_code, error_step)
No-op. Returns True.
end_session(session_id, total_rounds, total_duration, exit_reason)
No-op. Returns True.
Analytics tracking disabled. No data is collected or transmitted.