Exploring Advanced Techniques for System Prediction: An in-depth review

System prediction involves forecasting future states or behaviors of complex systems, which could range from stock market trends and weather patterns to hardware performance and network traffic. Depending on the specific domain and nature of the data, different techniques can be employed. This article surveys the different strategies for system prediction such as: (a) Statistical Models, (b) Time Series Analysis, (c) Machine Learning, (d) Deep Learning, (e) Simulation Models, and (f) Hybrid Approaches. The current status of research in each strategy is examined, and the major problems are identified. Finally, we identify future research directions in the realm of system prediction based on our in-depth investigation.