To forecast data that is constrained to the interval $]0,1[$, you can first logistically transform the observations: $ z_t = \ln\big(\frac{y_t}{1-y_t}\big) $. The transformed variables $z_t$ are then unconstrained and can be modeled and forecasted using any method you like (exponential smoothing, state spaces, ARIMA etc.). You then just back-transform the forecasts. However, this is only a first approximation, as the transformation may mess up your underlying distributional assumptions, but it may be a good first step. Here is one presentation by some very smart people on this.

Unfortunately, I can't help you with SPSS, sorry...

To forecast data that is constrained to the interval $]0,1[$, you can first logistically transform the observations: $ z_t = \ln\big(\frac{y_t}{1-y_t}\big) $. The transformed variables $z_t$ are then unconstrained and can be modeled and forecasted using any method you like (exponential smoothing, state spaces, ARIMA etc.). You then just back-transform the forecasts. However, this is only a first approximation, as the transformation may mess up your underlying distributional assumptions, but it may be a good first step. You may want to take a look at Snyder et al. (2017, IJF), "Forecasting compositional time series: A state space approach".

Unfortunately, I can't help you with SPSS, sorry...