Introduction to Time-series Forecasting

Overview

Questions

• How can we predict future values in a time-series?

Objectives

• Define concepts applicable to forecasting models.

Introduction

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This lesson is the second in a series of lessons demonstrating Python libraries and methods for time-series analysis and forecasting.

The first lesson, Time Series Analysis of Smart Meter Power Consmption Data, introduces datetime indexing features in the Python Pandas library. Other topics in the lesson include grouping data, resampling by time frequency, and plotting rolling averages.

This lesson introduces forecasting time-series. Specifically, this lesson aims to progressively demonstrate the attributes and processes of the SARIMAX model (Seasonal Auto-Regressive Integrated Moving Average witheXogenous factors). Exogenous factors are out of scope of the lesson, which is structured around the process of predicting a single timestep of a variable based on the the historic values of that same variable. Multi-variate forecasts are not addressed. Relevant topics include:

• Stationary and non-stationary time-series
• Auto-regression
• Seasonality

The lesson demonstrates statistical methods for testing for the presence of stationarity and auto-regression, and for using the SARIMAX class of the Python statsmodels library to make forecasts that account for these processes.

As noted throughout the lesson, the code used in this lesson is based on and in some cases is a direct application of code used in the Manning Publications title, Time series forecasting in Python, by Marco Peixeiro.

Peixeiro, Marco. Time Series Forecasting in Python. [First edition]. Manning Publications Co., 2022.

The original code from the book is made available under an Apache 2.0 license. Use and application of the code in these materials is within the license terms, although this lesson itself is licensed under a Creative Commons CC-BY 4.0 license. Any further use or adaptation of these materials should cite the source code developed by Peixeiro:

Peixeiro, Marco. Timeseries Forecasting in Python [Software code]. 2022. Accessed from https://github.com/marcopeix/TimeSeriesForecastingInPython.

The third lesson in the series is Machine Learning for Timeseries Forecasting with Python. It follows from and builds upon concepts from these first two lessons in the series.

All three lessons use the same data. For information about the data and how to set up the environment so the code will work without the need to edit file paths, see the Setup section.

Key Points

• The Python statsmodels library includes a full featured implementation of the SARIMAX model.