Time Series Analysis
Definition
Time series analysis examines temporal data sequences to describe patterns and predict developments.
Methods for describing, modeling, forecasting, and monitoring temporally dependent data.
Simply explained
You look at values over time and search for recurring patterns, trends, or unusual changes.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A medium-sized company models demand with seasonality, holidays, and forecast intervals.
Limits and pitfalls
- Random data splitting causes leakage; structural breaks, uncertainty, and naive baselines must be considered.
Origin & history
ARIMA, state-space models, and exponential smoothing shaped the field; ML methods complemented it.
Use in mid-sized companies
- Possible application: A medium-sized company models demand with seasonality, holidays, and forecast intervals.
Frequently asked questions
What does time series analysis mean?
Methods for describing, modeling, forecasting, and monitoring temporally dependent data. What should be considered? Random data splitting causes leakage; structural breaks, uncertainty, and naive baselines must be taken into account.
Sources
- Forecasting: Principles and Practice · Hyndman and Athanasopoulos, Forecasting: Principles and Practice
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: