In this project, three primary problems were addressed to improve battery data management and software performance evaluation. All solutions used voltage values in time together with various device characteristics. Battery replacement labeling was performed using Hidden Markov Models. Both deep reinforcement learning, specifically TD3 with an LSTM layer, and traditional models were employed to predict future battery voltages. These predictions subsequently informed a developed novel method for early evaluation of software impact on battery performance. A baseline model was also introduced for optimal battery replacement timing. Results indicated that the TD3-LSTM model achieved a mean absolute percentage error below 5%, on par with traditional methods. The battery replacement labeling had above 85% correctly labeled replacements, impact on battery performance was above 90% correct in software comparisons. TD3-LSTM proved a viable choice for multi-step predictions requiring online learning, albeit requiring potentially more tuning. / I detta projekt behandlades tre primära problem i syfte att förbättra batteridatahantering och utvärdering av mjukvaruprestanda. Alla lösningar använde spänningsvärden i tid tillsammans med olika enhetsegenskaper. Batteribytesmärkning utfördes med hjälp av Hidden Markov Models. Både deep reinforcement learning, särskilt TD3 med ett LSTM-lager, och traditionella modeller användes för att förutsäga framtida batterispänningar. Dessa förutsägelser användes sedan i en framtagen ny metod för tidig utvärdering av mjukvarans påverkan på batteriprestanda. En basmodell introducerades också för optimal batteribytestid. Resultaten indikerade att TD3-LSTM modellen uppnådde ett genomsnittligt absolut procentfel under 5%, i nivå med traditionella metoder. Batteribytesmärkningen hade över 85% korrekt märkta batteribyten, inverkan på batteriprestanda var över 90% korrekt i mjukvarujämförelser. TD3-LSTM visade sig vara ett hållbart val för flerstegsförutsägelser som kräver onlineinlärning, även om det krävde potentiellt mer justering.

Time series prediction is one of the main areas of statistics and machine learning. In 2018 the two new algorithms higher order hidden Markov model and temporal convolutional network were proposed and emerged as challengers to the more traditional recurrent neural network and long-short term memory network as well as the autoregressive integrated moving average (ARIMA). In this study most major algorithms together with recent innovations for time series forecasting is trained and evaluated on two datasets from the theme park industry with the aim of predicting future number of visitors. To develop models, Python libraries Keras and Statsmodels were used. Results from this thesis show that the neural network models are slightly better than ARIMA and the hidden Markov model, and that the temporal convolutional network do not perform significantly better than the recurrent or long-short term memory networks although having the lowest prediction error on one of the datasets. Interestingly, the Markov model performed worse than all neural network models even when using no independent variables.