Time series are a pivotal component of data analysis. This series goes through how to handle time series visualization and forecasting in Python 3.
In this tutorial, we will introduce some common techniques used in time-series analysis and walk through the iterative steps required to manipulate and visualize time-series data.
In this tutorial, we will produce reliable forecasts of time series. We will begin by introducing and discussing the concepts of autocorrelation, stationarity, and seasonality, and proceed to apply one of the most commonly used method for time-series forecasting, known as ARIMA.
This tutorial shows how to produce time series forecasts using the Prophet library in Python 3.