Time series analysis follows a structured process that helps ensure transparency, reproducibility and meaningful forecasting results.
1. Problem Definition
The process begins by clarifying the question the forecast should support. This includes the forecast horizon, required accuracy and relevant influencing factors.
2. Data Review and Preparation
The time series is examined for completeness, outliers, structural breaks and seasonal patterns. Missing values, anomalies and transformations are handled carefully to preserve the nature of the data.
3. Exploratory Analysis
Key properties such as trend, seasonality, autocorrelation and external drivers are analyzed to narrow down suitable model families.
4. Model Selection
Depending on the problem, classical statistical models, hybrid approaches or modern machine learning and deep learning methods may be appropriate. The choice depends on data availability, interpretability and pattern complexity.
5. Training and Validation
Models are trained on historical data and evaluated using a time consistent validation strategy, for example rolling or expanding windows. This tests how well the model generalizes to unseen future data.
6. Comparison and Selection
Multiple models are compared using quantitative metrics. Forecast accuracy, stability, robustness and transparency play a key role.
7. Interpretation
Forecasts are interpreted in context. Depending on the method, components such as trend, seasonality or the impact of external variables can be examined separately.
8. Documentation
All steps are documented to ensure the analysis remains traceable and reproducible over time.