Artificial intelligence enhances time-series analysis — a method for understanding data points arranged over time, such as daily sales, hourly traffic, or weekly demand. Time-series projects typically involve two main goals: forecasting future values (predicting what will happen next) and anomaly detection (spotting unusual patterns that deviate from expected behavior). A clear definition of the task and decision horizon — such as what you’re predicting and over what time frame — helps teams frame their approach effectively.
Preparing data is a crucial early step. Teams ensure timestamps are formatted consistently and handle issues like missing values, duplicates, and uneven intervals. Data is cleaned and transformed into features that help AI models learn patterns, such as lag values, rolling averages, and calendar indicators (e.g., day of the week). Starting with a small, meaningful feature set often simplifies training and improves model performance before adding more complexity.
When choosing models, it’s useful to begin with baseline approaches — such as repeating the last observed value or a seasonal equivalent — to set a minimum performance standard. Then teams move on to machine learning models that use engineered features, and potentially deep learning models for more complex patterns. For anomaly detection, teams can use the prediction error from a forecasting model as a signal: large deviations between predicted and actual values often indicate unusual events.
Evaluating results involves time-aware testing — training on earlier periods and validating on later periods to ensure stability over time — and selecting metrics like MAE, RMSE, and MAPE that align with business needs. After deployment, monitoring for data drift and retraining models as conditions change helps keep performance reliable. Thorough documentation of data windows, features, splits, and metrics also makes the work repeatable and transparent across teams.
