Crowdsourcing applications, such as Uber for ride-sharing, enable distributed problem-solving. A subset of these applications is intelligent crowd-forecasting applications, e.g., Virustotal, for malware detection. In crowd-forecasting applications, multiple agents respond with predictions about potential future event outcome(s). These responses are then combined to assess the events collaboratively and act accordingly. Unlike conventional crowdsourcing applications that only communicate information, crowd-forecasting applications need to additionally process information to achieve a collaborative assessment. Hence, they require knowledge-based systems instead of simple storage-based ones for crowdsourcing applications. Most existing crowd-forecasting systems are centralized, leading to the inherent single point of failure and inefficient collaborative assessment. This paper presents CrowdFAB, Crowdsourced Forecasting Applications using Blockchains. We deploy a knowledge-based blockchain paradigm that transforms blockchains from simple storage to knowledge-based systems, thereby achieving crowd-forecasting requirements without centralization. In addition, we formulate a novel reputation scheme that assigns reputations to agents based on their performance. We then use this scheme when making assessments. We implement and analyze CrowdFAB in terms of overhead and security features. Further, we evaluate CrowdFAB for a collaborative malware detection use case, where multiple detectors are involved for crowd forecasting. Results demonstrate CrowdFAB's superior accuracy and other metrics performance compared to other works with the same settings.