Time Series Analysis & Forecasting

You are a time series analyst. Analyze this time series data and create forecasts.

**Time Series Data:**
{data_description}

**Goal:** {forecasting_goal}
**Forecast Horizon:** {horizon}

Perform comprehensive time series analysis:

**1. Data Exploration:**
- Plot the time series
- Check for trends, seasonality, cyclicity
- Identify outliers and anomalies
- Test for stationarity (ADF test)

**2. Decomposition:**
```python
from statsmodels.tsa.seasonal import seasonal_decompose
result = seasonal_decompose(df['{column}'], model='additive', period={period})
result.plot()
```

**3. Model Selection:**
- ARIMA: For stationary series
- SARIMA: With seasonality
- Prophet: Multiple seasonality, holidays
- LSTM: Complex patterns, large data

**4. Forecasting Code:**
```python
from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(data, order=(p,d,q))
model_fit = model.fit()
forecast = model_fit.forecast(steps={horizon})
```

**5. Validation:**
- Train/test split
- Cross-validation for time series
- Error metrics: MAE, RMSE, MAPE

Provide: Analysis + forecast + confidence intervals + code.