Time series analysis hourly data Python SARIMAX or better another ML-Algorithm

I am working on my bachelor thesis with time series data. The idea is to predict the expected battery life based on voltage data from sensors. During my research I came across SARIMAX. For me this ML algorithm sounded very plausible at first. Unfortunately, I was only able to generate constant predictions. Since I was not sure if this prediction was due to the underlying possibly incomplete data set. I Calculated a data set with charge and discharge curves myself. So the data set my questions refer to looks like this: Before passing the data to the algeorithm for learning, I logarithmized the data, formed the firstdifference, and tried to clean up the difference in seasonality. When I create a prediction with SARAMIAX I get only one constant like here: My goal is to continue writing the curve into the future something like this: I have read in some examples that it is not an error of the Sarimax model, but since the prediction only refers to the previous value, only a constant can be predicted. Now, of course, I'm wondering whether I'm on the wrong track with SARIMAX, or whether I've simply taught the model incorrectly and can continue to work with SARIMAX. Maybe there is another ML algorithm you would prefer for this task? Maybe someone reads this post who has experience with the prediction of time series data and puts me back on the right track. I appreciate any kind of feedback, thank you in advance. Edit: The original data is transmitted by the sensors every 15 min, I re…