預營指

預來資用與系指以謀容與優費。

見 Extended Examples 備全設與模。

用

• 預基容求（CPU、記、碟、網）
• 為下季謀硬/雲資購
• 欲預費軌以優雲費
• 須依預載設先動擴策
• 預用者流以謀事
• 預庫存長以備份
• 估 API 用以設速限

入

• 必：歷時序指（最少 3-12 月）
• 必：指類（CPU、記、請求/秒、費等）
• 必：預域（前 N 日、週、或月）
• 可：已知來事（布、廣、假）
• 可：季信（日、週、年模）
• 可：外回歸（如廣費、用註冊）

行

一：設環境並載數

裝預庫並備時序數。

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install forecasting libraries
pip install prophet statsmodels pandas numpy
pip install plotly matplotlib seaborn
pip install prometheus-api-client influxdb-client
pip install grafana-api

以 MetricsLoader 載並備：

# forecasting/data_loader.py (abbreviated)
import pandas as pd
from datetime import datetime, timedelta

class MetricsLoader:
    def load_from_prometheus(self, query: str, lookback_days: int = 90, step: str = "1h"):
        """Load historical metrics from Prometheus."""
        # ... implementation (see EXAMPLES.md for complete code)

    def resample_and_aggregate(self, df: pd.DataFrame, freq: str = "1H"):
        """Resample time series to regular intervals."""
        # ... implementation (see EXAMPLES.md)

# Example usage
loader = MetricsLoader(prometheus_url="http://prometheus:9090")
df = loader.load_from_prometheus(
    query='avg(rate(container_cpu_usage_seconds_total[5m]))',
    lookback_days=90,
)
df_daily = loader.resample_and_aggregate(df, freq="1D")

全 MetricsLoader 實現見 EXAMPLES.md Step 1。

得：時序數以規隔載、缺值已填、備供預。

敗：數有隙→用前填或插值；確回期有足數（建 90+ 日）；驗時戳時區一致；察偏歪預之離群（>5 sigma）。

二：施 Prophet 預

用 Facebook Prophet 自動察季並預。

# forecasting/prophet_forecaster.py (abbreviated)
from prophet import Prophet

class ProphetForecaster:
    def __init__(self, growth: str = "linear", seasonality_mode: str = "multiplicative"):
        self.growth = growth
        self.prophet_params = {
            "growth": growth,
            "seasonality_mode": seasonality_mode,
            # ... additional parameters (see EXAMPLES.md)
        }

    def fit(self, df: pd.DataFrame, regressors=None, holidays=None):
        """Train Prophet model on historical data."""
        # ... implementation (see EXAMPLES.md)

    def forecast(self, periods: int, freq: str = "D"):
        """Generate forecast for future periods."""
        # ... implementation (see EXAMPLES.md)

# Example usage
forecaster = ProphetForecaster(growth="linear", seasonality_mode="multiplicative")
forecaster.fit(df_daily)
forecast = forecaster.forecast(periods=30, freq="D")
forecaster.plot_forecast(forecast, save_path="results/cpu_forecast.png")

全 ProphetForecaster 實現見 EXAMPLES.md Step 2。

得：前 30+ 日預含信區已生、季模於件圖捕、交驗 MAPE < 15%。

敗：預不實→試異長模（線對邏）；季缺→調 seasonality_mode；準劣（<70% MAPE）→加歷數或外回歸，察數質議。

三：施 ARIMA/SARIMAX 預（替）

用 statsmodels 傳時序預。

# forecasting/arima_forecaster.py (abbreviated)
from statsmodels.tsa.statespace.sarimax import SARIMAX

class ARIMAForecaster:
    def __init__(self, order: tuple = (1, 1, 1), seasonal_order: tuple = (1, 1, 1, 7)):
        self.order = order
        self.seasonal_order = seasonal_order

    def fit(self, df: pd.DataFrame, exog=None):
        """Train SARIMAX model."""
        series = df.set_index("timestamp")["value"]
        self.model = SARIMAX(series, exog=exog, order=self.order, seasonal_order=self.seasonal_order)
        self.fitted_model = self.model.fit(disp=False)
        # ... implementation (see EXAMPLES.md)

    def forecast(self, steps: int, exog_future=None):
        """Generate forecast for future periods."""
        # ... implementation (see EXAMPLES.md)

# Auto-select parameters
best_order, best_seasonal = auto_arima(series, seasonal=True)
forecaster = ARIMAForecaster(order=best_order, seasonal_order=best_seasonal)
forecaster.fit(df_hourly)
forecast = forecaster.forecast(steps=168)  # 7 days

全 ARIMAForecaster 實現與 auto_arima 函見 EXAMPLES.md Step 3。

得：ARIMA 模以最優參擬、預含信區生、診圖示白噪殘。

敗：模不收→簡參（減 p、q、P、Q）；預軌誤→察差階（d、D）；殘非白噪→加 AR/MA 項；確序長 >2x 季週。

四：識容門與警

析預以知資何時竭。

# forecasting/capacity_planning.py (abbreviated)
from datetime import datetime

class CapacityPlanner:
    def __init__(self, capacity_limit: float, warning_threshold: float = 0.8):
        self.capacity_limit = capacity_limit
        self.warning_threshold = warning_threshold

    def find_exhaustion_date(self, forecast: pd.DataFrame):
        """Find when forecast exceeds capacity limit."""
        exceeded = forecast[forecast["yhat"] >= self.capacity_limit]
        # ... implementation (see EXAMPLES.md)

    def generate_capacity_report(self, forecast: pd.DataFrame):
        """Generate comprehensive capacity planning report."""
        # ... implementation (see EXAMPLES.md)

# Example usage
planner = CapacityPlanner(capacity_limit=1000, warning_threshold=0.8)
report = planner.generate_capacity_report(forecast)
print(f"Warning Date: {report['warning_date']}")
print(f"Exhaustion Date: {report['exhaustion_date']}")
recommendation = planner.recommend_scaling_action(report)

全 CapacityPlanner 實現見 EXAMPLES.md Step 4。

得：報示容限達時、建含急度、長率已算。

敗：竭日不實→驗 capacity_limit 正；長率過高→察歷數離群；熟系考非線長模。

五：於 Grafana 視預

推預數於 Grafana 以實時監。

# forecasting/grafana_integration.py (abbreviated)
import requests

class GrafanaForecaster:
    def __init__(self, grafana_url: str, api_key: str, dashboard_uid: str = None):
        self.grafana_url = grafana_url.rstrip("/")
        self.api_key = api_key
        self.dashboard_uid = dashboard_uid

    def create_annotation(self, text: str, tags: list, time: datetime = None):
        """Create annotation in Grafana for forecast events."""
        # ... implementation (see EXAMPLES.md)

    def create_capacity_alert_annotation(self, capacity_report: dict):
        """Create Grafana annotation for capacity warnings."""
        # ... implementation (see EXAMPLES.md)

# Export to CSV for Grafana datasource
def export_forecast_to_csv(forecast: pd.DataFrame, output_path: str):
    """Export forecast in format compatible with Grafana CSV datasource."""
    # ... implementation (see EXAMPLES.md)

# Example usage
grafana = GrafanaForecaster(
    grafana_url="http://grafana:3000",
    api_key="YOUR_API_KEY",
    dashboard_uid="your-dashboard-uid",
)
grafana.create_capacity_alert_annotation(report)
export_forecast_to_csv(forecast, "grafana/forecasts/cpu_forecast.csv")

全 GrafanaForecaster 實現見 EXAMPLES.md Step 5。

得：預注現於 Grafana 儀表、容警見為直標、預數經 CSV 源可取。

敗：驗 Grafana API 鑰權正；察儀 UID 正；確注時戳為毫秒；與管合前以 curl 試 API。

六：自動生預

設表作常生預。

# forecasting/scheduler.py (abbreviated)
import schedule
import time

def generate_daily_forecast():
    """Generate forecast for all monitored metrics."""
    logger.info("Starting daily forecast generation")

    metrics_config = [
        {"name": "cpu_usage", "query": "...", "capacity_limit": 0.8, "forecast_days": 30},
        {"name": "memory_usage", "query": "...", "capacity_limit": 32, "forecast_days": 30},
        {"name": "disk_usage", "query": "...", "capacity_limit": 500, "forecast_days": 90},
    ]

    loader = MetricsLoader(prometheus_url="http://prometheus:9090")

    for metric_config in metrics_config:
        df = loader.load_from_prometheus(query=metric_config["query"], lookback_days=90)
        forecaster = ProphetForecaster()
        forecaster.fit(df)
        forecast = forecaster.forecast(periods=metric_config["forecast_days"])

        planner = CapacityPlanner(capacity_limit=metric_config["capacity_limit"])
        report = planner.generate_capacity_report(forecast)

        export_forecast_to_csv(forecast, f"grafana/forecasts/{metric_config['name']}_forecast.csv")
        # ... (see EXAMPLES.md for complete implementation)

# Schedule daily at 2 AM
schedule.every().day.at("02:00").do(generate_daily_forecast)

while True:
    schedule.run_pending()
    time.sleep(60)

全表作實現見 EXAMPLES.md Step 6。

得：諸指預日生、容報已記、CSV 為 Grafana 出、要容警已發。

敗：驗表作續運（用 systemd/supervisor）；察 Prometheus 連；確碟足空為預出；施暫敗重試；設監於表作本身。

驗

• 歷數含 90+ 日續指已載
• Prophet 預於件圖捕日/週季
• 預信區於驗含 85-95% 實值
• 已知景之容竭日算正
• ARIMA 模殘於診圖似白噪
• Grafana 注現於預警/竭日
• 自動預日運無須人干
• 預準（MAPE）於驗集 <15%

忌

• 歷數不足：3-12 月方可信季察；<60 日勿預
• 忽已知事：假、布、廣歪預；加為外回歸或假
• 長期預過信：>30-90 日準降；為向導非精預
• 容限靜：基設隨時變；加資源時更 capacity_limit
• 異常預：訓數離群傳於預；清數或用穩法
• 不更模：系變後預舊；週重訓或大架變後重訓
• 忽信區：點預誤導；謀恒用上下界
• 錯季週：時數用日、日數用週；錯致預劣

參

• detect-anomalies-aiops - 異察補預供先動監
• plan-capacity - 基容謀流
• build-grafana-dashboards - 視預與容軌