china-export-analytics（中国出口分析）

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.*