What are the limitations of time series analysis?

Time series analysis has several key limitations that developers should understand when working with temporal data. First, many time series models rely on strict assumptions that real-world data often violates. For example, methods like ARIMA assume stationarity (constant mean and variance over time), but trends, seasonality, or abrupt changes (e.g., a pandemic disrupting sales data) can invalidate this. Even after differencing or transformations, residual non-stationarity can lead to poor forecasts. Missing data or irregular sampling intervals—common in IoT sensor data or user activity logs—also pose challenges. Techniques like interpolation or imputation can introduce bias, while ignoring gaps may distort patterns like seasonality.

Second, model selection and parameter tuning require careful trade-offs. Models like SARIMA or Prophet demand expertise to configure parameters (e.g., order of differencing, seasonality components). For instance, choosing the wrong lag order in an ARIMA model might capture noise instead of true patterns, leading to overfitting. Deep learning approaches (e.g., LSTMs) can automate feature extraction but require large datasets and computational resources, making them impractical for low-latency applications or small-scale projects. Additionally, many models struggle with multi-step forecasting: errors compound as predictions extend further into the future, as seen in weather models where day-five forecasts are far less reliable than day-one.

Finally, time series analysis often fails to account for external factors or causal relationships. While models can detect correlations in historical data (e.g., ice cream sales and drowning incidents both peaking in summer), they don’t inherently distinguish causation from coincidence. Incorporating external variables (e.g., marketing spend, economic indicators) improves accuracy but requires domain knowledge to select relevant features. For example, a retail demand forecast ignoring supply chain disruptions or competitor pricing will likely miss sudden shifts. Moreover, black-box models like neural networks lack interpretability, making it harder to diagnose why a prediction failed—a critical issue in domains like healthcare or finance where explainability matters.