# TFCI (Time Forecasting CI)
간단한 시계열 예측 라이브러리
### 기본 라이브러리 (tfci)
```bash
pip install tfci
```
## 사용법
### 기본 라이브러리 사용
```python
from tfci import predict
# YAML 설정 파일로 예측 실행
predict("config.yaml")
```
## 기능
### tfci 라이브러리
- ✅ 시계열 예측 (Prophet 기반)
- ✅ YAML 설정 파일 지원
- ✅ DB2 데이터베이스 연동
- ✅ group_key 리스트 지원
- ✅ 멀티프로세싱 지원
### tfci-mcp 패키지
- ✅ MCP (Model Context Protocol) 지원
- ✅ 다른 MCP들과 조합 가능
- ✅ JSON-RPC 통신
- ✅ 서버/클라이언트 분리
## 설정 파일 예시
```yaml
input:
source_type: "db" # or csv
db_type: "db2"
connection:
host: "DBURL"
port: PortNumber
user: "UserName"
password: "Pwd!"
database: "DBName"
table: "TableName"
features: ["COL_1", "COL_2", "COL_3"]
target: ["COL_4"]
prediction:
task_type: "timeseries"
future_steps: 5 # 예측 구간 (5년 후)
time_col: "COL_1"
group_key: ["COL_2", "COL_3"]
output:
source_type: "db" # or csv
db_type: "db2"
connection:
host: "DBURL"
port: PortNumber
user: "UserName"
password: "Pwd!"
database: "DBName"
table: "TableName"
```
## 의존성
### tfci
- pandas>=2.0.0
- numpy>=1.20.0
- prophet>=1.1.0
- scikit-learn>=1.0.0
- tqdm>=4.60.0
- PyYAML>=6.0
- requests>=2.25.0
### tfci-mcp
- tfci>=1.0.4
## 라이선스
MIT License
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