To set up our environment for time-series forecasting, let’s first move into our local programming environment or server-based programming environment:įrom here, let’s create a new directory for our project. If you do not have it already, you should follow our tutorial to install and set up Jupyter Notebook for Python 3. To make the most of this tutorial, some familiarity with time series and statistics can be helpful.įor this tutorial, we’ll be using Jupyter Notebook to work with the data. Working with large datasets can be memory intensive, so in either case, the computer will need at least 2GB of memory to perform some of the calculations in this guide.
CODE ON TIME COMMUNITY HOW TO
This guide will cover how to do time-series analysis on either a local desktop or a remote server.
Here, we will primarily focus on the ARIMA component, which is used to fit time-series data to better understand and forecast future points in the time series. One of the methods available in Python to model and predict future points of a time series is known as SARIMAX, which stands for Seasonal AutoRegressive Integrated Moving Averages with eXogenous regressors.
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. In this tutorial, we will aim to produce reliable forecasts of time series. The specific properties of time-series data mean that specialized statistical methods are usually required. Based on previous values, time series can be used to forecast trends in economics, weather, and capacity planning, to name a few. Time series provide the opportunity to forecast future values.