How can I improve the accuracy of a time series forecast with limited historical data?

Example Concept: One effective approach is to use feature engineering to extract additional insights from the limited data. This can involve creating lag features, moving averages, or seasonal indicators that capture underlying patterns. Additionally, incorporating external data such as economic indicators, weather data, or industry-specific variables can provide valuable context and improve forecast accuracy. Another strategy is to apply ensemble methods that combine predictions from multiple models to reduce variance and improve robustness.