In time series forecasting, which method involves using past observations as inputs for predicting future values?
- Regression Analysis
- ARIMA (AutoRegressive Integrated Moving Average)
- Principal Component Analysis (PCA)
- k-Nearest Neighbors (k-NN)
ARIMA is a time series forecasting method that utilizes past observations to predict future values. It incorporates autoregressive and moving average components, making it suitable for analyzing time series data. The other options are not specifically designed for time series forecasting and do not rely on past observations in the same way.
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