TD_ACF calculates the autocorrelation or autocovariance of a time series. Autocorrelation and autocovariance show how the time series correlates or covaries with itself when delayed by a lag in time or space. When TD_ACF is computed, a coefficient corresponding to a particular lag is affected by all the previous lags. For example, the coefficient for lag 4 includes effects of activity at lags 3, 2, and 1.
The function is useful for identifying patterns and dependencies in time series data. For example, a positive autocorrelation at lag 1, indicates that the value of a time series at time t is positively correlated with the value at time t-1, suggesting a trend or momentum in the data. On the other hand, negative autocorrelation at lag 1 indicates a reversal or mean reversion in the data.
TD_ACF can determine the appropriate lag order for an autoregressive (AR) or moving average (MA) model. If TD_ACF shows a significant positive or negative correlation at a given lag, it suggests that the model should include that lag as a predictor variable.
In addition to the TD_ACF, there is TD_PACF (partial autocorrelation function) that measures the correlation between a time series and its lagged values while controlling for the effects of intermediate lags.