  Time-series forecasting has been an important research domain for so many
years. Its applications include ECG predictions, sales forecasting, weather
conditions, even COVID-19 spread predictions. These applications have motivated
many researchers to figure out an optimal forecasting approach, but the
modeling approach also changes as the application domain changes. This work has
focused on reviewing different forecasting approaches for telemetry data
predictions collected at data centers. Forecasting of telemetry data is a
critical feature of network and data center management products. However, there
are multiple options of forecasting approaches that range from a simple linear
statistical model to high capacity deep learning architectures. In this paper,
we attempted to summarize and evaluate the performance of well known time
series forecasting techniques. We hope that this evaluation provides a
comprehensive summary to innovate in forecasting approaches for telemetry data.
