Univariate time series models for forecasting stationary and non-stationary data: A brief review

Abstract

Due to advancements in domain of Information processing, huge amount of data gets collected which varies according to different time intervals. Structural models and Time-series models are used for analysing time series data. Time series models are very efficient as compared to structural models because modelling and predictions can be easily done. This paper gives a brief insight into Auto-regressive Models (AR), Moving Average Models (MA), Autoregressive Moving Average model (ARMA) and Autoregressive Integrated Moving Average Model (ARIMA). This paper also helps to understand the characteristics of the data which will be used for Time-series modelling.