Predicting the future is an essential element in many fields, as it can lead to significant cost savings due to more efficient resource allocation. Modern Cloud systems are composed of large numbers of processing and storage resources, giving the potential to dynamically scale the resources provided to workloads throughout the day. If the amount of resources required is known in advance, proactive scaling can be implemented, with the aim that only the required amount of resources are allocated, resulting in optimal performance and cost efficiency. This study aims to accurately and efficiently forecast the workload of a financial system, characterized by high frequency, noise, and unpredictability. Based on the dataset describing the historical workload for individual tasks within the market system, a model within each category, statistical, machine learning, and artificial neural network was implemented. The models within each category with the most promising qualities for such data were, SARIMA, XGBoost, and LSTM. These 3 models were compared on different scenarios with a focus on the trade-off between accuracy and complexity. An optimal model has a low complexity which means efficient but still has a high accuracy. The result of this study showed that the workload within this specific system can be predicted using the 3 models. The optimal model varies depending on scaling requirements, for short-term high-accuracy predictions LSTM is the best with an R2 score of 0.92, but also the most complex. XGBoost is less complex than LSTM and has an overall better accuracy on different scenarios. SARIMA, though simpler, exhibits the best accuracy for long-term predictions with an R2 score of 0.75. This study concludes that it is possible to predict certain financial workloads in advance, paving the way for further research into proactive scaling in such scenarios.