Exploring Time Series Forecasting
A comprehensive introductory guide to time series forecasting covering fundamental concepts like autocorrelation, trend, seasonality, and stationarity. The article walks through the complete forecasting workflow—from data collection and preprocessing to model selection and evaluation—with a practical SARIMA implementation.
This article was originally published on Medium as part of the Build Better with SynergyBoat publication.
Overview
Time series forecasting is a powerful tool in data-driven decision-making, particularly in critical sectors like electric vehicle infrastructure planning. This comprehensive guide covers:
- Fundamentals: Autocorrelation, trend, seasonality, stationarity, and noise
- Data Preprocessing: Visualization, stationarity tests (ADF), transformations, and decomposition
- Feature Engineering: Lag features, rolling statistics, time-based features, and exogenous variables
- Model Selection: ARIMA/SARIMA, Exponential Smoothing, Machine Learning (XGBoost, Random Forest), and Deep Learning (LSTM, TFT)
- Evaluation Metrics: MAE, RMSE, MAPE, SMAPE, WAPE
Practical Implementation
The article includes a hands-on example using the IPG2211A2N Industrial Production of Electric and Gas Utilities dataset from the Federal Reserve, demonstrating a complete SARIMA implementation that achieves 80% R-squared with less than 4% MAPE.