The study aimed to define and investigate relevant parameters affecting manual frequency restoration reserve (mFRR) volumes of the balancing market in the Finnish price area. It also aimed to find suitable models and investigate Day-ahead prediction possibilities of mFRR volumes. Parameters related to mFRR volumes Day-ahead predictions were identified in several earlier studies where of nine parameters were investigated. The correlations between mFRR volumes and the different parameters were investigated using Spearman’s correlation. Different linear machine learning models for Day-ahead predictions of mFRR volumes were builtand tested in Python. The resulting models used for predicting mFRR volumes in Python were one ARIMAX model and one SARIMAX model. The models were validated with a walk-forward method where Day-ahead predictions were conducted monthly for one year. The accuracy of the predictions was measured by the validation parameters Mean Absolute Value, Root Mean Square Error and Median Absolute Deviation. Results from the study show that it is difficult to predict absolute activated mFRR volumes. Although, it might be possible to predict that mFRR volumes will be activated or not, up- or down regulated to some extent. One explanation of the difficulties in predicting mFRR volumes is dueto mFRR being a balancing product whose function is to regulate disturbances in the electricity grid.