Time Series Analysis (TSA) is an important part in the field of data science. TSA uses methods for analyzing time series data in order to identify useful patterns and extract meaningful statistics of the data. There are two major goals of TSA: 1) identifing patterns or features represented by the data; and 2) forecasting (using a model to predict future values based on previous data). In this article, we will do a complete machine learning pipeline on analysis time series data. We will use both ARIMA model and Prophet model to predict superstore sales data. These two models are very important in analyzing time series data.

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