Time Series Analysis & Forecasting

The best way to predict the future is to study the past. — Robert Kiyosaki

Introduction

Time series analysis focuses on data ordered by time. Forecasting future values is a core ML capability in business contexts.

What You Will Learn

• Understand time series components: trend, seasonality, noise
• Test for and achieve stationarity
• Build ARIMA and SARIMA models
• Apply Facebook Prophet for automated forecasting

Assessment Connection

Section A — time series forecasting forms the core of many workplace ML projects. ARIMA, SARIMA, and Prophet are listed as suitable techniques.

Content

TutorialsHow-to GuidesReferenceExplanation

• Time Series Fundamentals
• Stationarity & Differencing
• ARIMA Models
• SARIMA (Seasonal ARIMA)
• Facebook Prophet

• Decompose a Time Series
• Evaluate Forecast Accuracy
• Handle Missing Timestamps

• Statsmodels TSA Reference
• Prophet Parameters
• ACF/PACF Interpretation Guide

• Why Stationarity Matters
• ML vs Statistical Forecasting