Vertical federated learning’s (VFL) immense potential for time series forecasting in industrial applications such as predictive maintenance and machine control remains untapped. Critical challenges to be addressed in the manufacturing industry include small and noisy datasets, model explainability, and stringent privacy requirements for training and inference of forecasting. Additionally, to increase industry adaptability, such forecasting models must scale well with the parties/clients while ensuring strong convergence and low tuning complexity. To this end, we propose and design “Secret-shared Time Series Forecasting with VFL” (STV), a novel framework with the following features: i) a privacy-preserving VFL algorithm for time series forecasters such as SARIMAX and autoregressive trees, ii) secret sharing with multi-party computation protocols for aggregating intermediate training data and for privacy-preserving serverless inference, iii) extension of secure two-party matrix operations for direct parameter optimization to multiple parties, giving strong convergence with minimal hyperparameter tuning complexity. We conduct evaluations on six diverse datasets from both public and industry-specific contexts. Our results demonstrate that STV’s forecasting accuracy is comparable to those of centralized approaches and that direct optimization can outperform centralized methods by 23.81% on forecasting accuracy, including state-of-the-art diffusion models and long-short-term memory. We also conduct scalability analysis to offer the opportunity for flexible decision-making by examining the communication costs of direct and iterative optimization, allowing navigating between these two approaches.