Multi-Layer Forecasting Model for Wireless Cell KPIs

The forecasting model is segmented into multiple independent layers: a linear pattern layer using double exponential smoothing, a periodic pattern layer using discrete Fourier transforms, and a residual pattern layer using stacked causal convolutional layers. Each layer handles specific aspects of the time-series data, allowing the system to capture complex relationships between multiple KPIs while maintaining manageable complexity through modular architecture.

The model transitions from univariate to multivariate analysis by stacking multiple layers that process different dimensions of the data. The first layer handles linear trends, the second layer captures periodicities, and the third layer identifies complex cross-KPI relationships. This dimensional approach enables the system to forecast multiple KPIs simultaneously while capturing their interdependencies.

The computational task is segmented across multiple specialized layers. The linear pattern layer processes trends using efficient double exponential smoothing, the periodic pattern layer handles seasonal variations through discrete Fourier transforms, and the residual layer captures complex relationships using stacked causal convolutional layers. This segmentation allows each layer to be optimized for its specific function, reducing overall computational burden compared to a single monolithic model.

The model replaces simple mechanical averaging methods with more sophisticated mathematical transformations. Double exponential smoothing substitutes basic moving averages to capture trends more accurately, discrete Fourier transforms replace simple periodic detection to identify seasonal patterns, and stacked causal convolutional layers replace traditional time-series analysis to model complex KPI relationships. These substitutions improve accuracy while maintaining computational efficiency through well-established algorithms.