The attention for personalized mental health care is thriving. Research data specific to the individual, such as time series sensor data or data from intensive longitudinal studies, is relevant from a research perspective, as analyses on these data can reveal the heterogeneity among the participants and provide more precise and individualized results than with group-based methods. However, using this data for self-management and to help the individual to improve his or her mental health has proven to be challenging. The present work describes a novel approach to automatically generate personalized advice for the improvement of the well-being of individuals by using time series data from intensive longitudinal studies: Automated Impulse Response Analysis (AIRA). AIRA analyzes vector autoregression models of well-being by generating impulse response functions. These impulse response functions are used in simulations to determine which variables in the model have the largest influence on the other variables and thus on the well-being of the participant. The effects found can be used to support self-management. We demonstrate the practical usefulness of AIRA by performing analysis on longitudinal self-reported data about psychological variables. To evaluate its effectiveness and efficacy, we ran its algorithms on two data sets ($N=4$ and $N=5$), and discuss the results. Furthermore, we compare AIRA's output to the results of a previously published study and show that the results are comparable. By automating Impulse Response Function Analysis, AIRA fulfills the need for accurate individualized models of health outcomes at a low resource cost with the potential for upscaling.