Time Series Forecasting methods suitable for prediction of CPU usage

Abstract

Time series data refers to data points that follow a chronological sequence or ordering. Modelling and analysis of such data is generally done to extract significant statistics and also to utilize the past historic data in order to predict future points. In this paper, three popular time series forecasting methods, namely Holt Winters, ARIMA and LSTMs, are applied on CPU data and their results are compared. LSTM is found to be more suited for predicting CPU usage followed by ARIMA. LSTM performs better due to the fact that CPU usage is unstable and has fluctuations even though it is seasonal in nature. By performing such an analysis, it is possible to identify patterns which help in predicting the usage of future resources for future demand which in turn enables optimization of resource management.