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You recover the original series $q_k$ by summing the predicted differences $Z_t$ and adding them to the last known actual value $q_l$ (or $q_1$ depending on the starting index). The formula is $q_k = \sum_{i=1}^{k-l} Z_{k-i} + q_l$.

Conditions: You have forecasts for the differenced series $Z_t$.; You know the last observed value of the original series $q_l$.; The relationship $Z_t = q_{t+1} - q_t$ holds.

You recover the original series $q_k$ by summing the predicted differences $Z_t$ and adding them to the last known actual value $q_l$ (or $q_1$ depending on the starting index). The formula is $q_k = \sum_{i=1}^{k-l} Z_{k-i} + q_l$.

Conditions: You have forecasts for the differenced series $Z_t$.; You know the last observed value of the original series $q_l$.; The relationship $Z_t = q_{t+1} - q_t$ holds.

Differencing is a technique used to transform a non-stationary time series into a stationary one by computing the differences between consecutive observations.

Conditions: Used when data exhibits non-stationarity.

Differencing is a technique used to transform a non-stationary time series into a stationary one by computing the differences between consecutive observations.

Conditions: Used when data exhibits non-stationarity.

The differenced series $z_t$ is defined as the difference between the next value of the original series and the current value. Mathematically, it is expressed as $z_t = q_{t+1} - q_t$.

Conditions: The original series is indexed by time $t$.; The values represent observations at discrete time points (e.g., months).

The differenced series $z_t$ is defined as the difference between the next value of the original series and the current value. Mathematically, it is expressed as $z_t = q_{t+1} - q_t$.

Conditions: The original series is indexed by time $t$.; The values represent observations at discrete time points (e.g., months).