Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments \& Analysis]
Prediction markets are markets for trading claims on future events, such as presidential elections, and their prices provide continuously updated signals of collective beliefs. In decentralized platforms such as Polymarket, the market lifecycle spans market creation, token registration, trading, oracle interaction, dispute, and final settlement, yet the corresponding data are fragmented across heterogeneous off-chain and on-chain sources. We present the first continuously maintained dataset suite for the full lifecycle of decentralized prediction markets, built on Polymarket. To address the challenges of large-scale cross-source integration, incomplete linkage, and continuous synchronization, we build a unified relational data system that integrates three canonical layers: market metadata, fill-level trading records, and oracle-resolution events, through identifier resolution, on-chain recovery, and incremental updates. The resulting dataset spans October 2020 to March 2026 and comprises more than 770 thousand market records, over 943 million fill records, and nearly 2 million oracle events. We describe the data model, collection pipeline, and consistency mechanisms that make the dataset reproducible and extensible, and we demonstrate its utility through descriptive analyses of market activity and two downstream case studies: NBA outcome calibration and CPI expectation reconstruction.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Recursive deep learning framework for forecasting the decadal world economic outlook
The gross domestic product (GDP) is the most widely used indicator in macroeconomics and the main tool for measuring a country's economic output. Due to the diversity and complexity of the world economy, a wide range of …
Deep LearningTemporal SequencesTime SeriesTime Series Analysis+1Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting
Unlocking the potential of deep learning in Peak-Hour Series Forecasting (PHSF) remains a critical yet underexplored task in various domains. While state-of-the-art deep learning models excel in regular Time Series Forec…
DecoderTime SeriesTime Series ForecastingSAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting. To better …
Time SeriesTime Series ForecastingMoTime: A Dataset Suite for Multimodal Time Series Forecasting
While multimodal data sources are increasingly available from real-world forecasting, most existing research remains on unimodal time series. In this work, we present MoTime, a suite of multimodal time series forecasting…
Time SeriesTime Series ForecastingGradient Boost with Convolution Neural Network for Stock Forecast
Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, whic…
Ensemble Learning