Final answer:
ACF and PACF are important tools in time series forecasting as they help us understand the relationship between current and past observations and identify influences of specific lags on forecasting.
Step-by-step explanation:
The ACF (Auto Correlation Function) and PACF (Partial Auto Correlation Function) are two important tools used in time series forecasting.
ACF measures the correlation between a variable and its lagged values. It helps us understand the relationship between the current observation and its past observations. A strong correlation at a specific lag suggests that there might be a pattern or trend that can be used for forecasting.
PACF, on the other hand, measures the direct correlation between a variable and its lagged values while controlling for the indirect correlations through other lags.
It helps us identify the influences of specific lags on the current observation, allowing for more accurate forecasting.