1) Which of these are not a components of time series data?

• Trend
• Seasonality
• Resid
• Noise

2) Which of these show the importance of time series analysis?

• Forecasting
• Prediction
• Anomaly Detection
• Decomposition

3) Which of these are the components of a seasonal decompose?

• Forecasting
• Trend
• Residual
• Decompose

4) Which of these are decomposition techniques?

• Moving averages
• Seasonal decomposition of time series
• Holt Winters Exponential Smoothing
• Seasonal decomposition using LOESS

5) Which of these are types of moving averages?

• SARIMA
• SMA
• WMA
• EMA

6) Which of these are types of Exponential Smoothing?

• Naive
• Holt
• Holt-Winters
• Winters

7) What are the advantages of Exponential Smoothing?

• Provide smoothed estimates of the underlying trend
• They are effective for reducing noise and identifying patterns.
• Adapts quickly to changing patterns in the data
• Calculating forecasts based on historical data

8) Which of these are examples of autocorrelation?

• DACF
• ACF
• TACF
• PACF

9) Which of these are examples of interpreting autocorrelation?

• ACF Interpretation
• PACF Interpretation
• Error Metrics
• Role in Model Selection

10) Which of these are examples of forecast evaluation metrics?

• MAE
• MSE
• MAPE
• RMSE