China Export Analytics

---

name: china-export-analytics description: > Advanced analytical toolkit for China export data. Provides statistical modeling, time-series forecasting, correlation analysis, and quantitative research methods for data scientists and analysts. version: 1.0.0 author: cxg language: en tags: [analytics, data-science, statistics, forecasting, trade-data, quantitative]

---

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.*