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Time Series Analysis Introduction — A Comparison of ARMA, ARIMA, SARIMA Models
On the differences between these models, and how you should use them
What is Time Series?
Time series is a unique type of problem in machine learning where the time component plays a critical role in the model predictions. As observations are dependent on adjacent observations, this violates the assumption that observations are independent to each other followed by most conventional machine learning models. Common use cases of time series analysis are forecasting future numeric values, e.g. stock pricing, revenue, temperature, which falls under the category of regression models. However, time series models can also be applied in classification problems, for instance, pattern recognition in brain wave monitoring, or failure identification in the production process are common applications of time series classifiers.
In this article, we will mainly focus on three time series model – ARMA, ARIMA, and SARIMA for regression problems where we forecast numeric values. Time series regression differentiates from other regression models, because of its assumption that data correlated over time and the outcomes from previous periods can be used for predicting the outcomes in the subsequent periods.
