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<url><loc>https://www.amazon.science/publications/mailto:?body=Impact-driven%20event%20embeddings%20for%20context-aware%20forecasting%20and%20anomaly%20detection%20in%20financial%20time%20series%0A%0Ahttps%3A%2F%2Fwww.amazon.science%2Fpublications%2Fimpact-driven-event-embeddings-for-context-aware-forecasting-and-anomaly-detection-in-financial-time-series%0A%0AFinancial%20transaction%20time%20series%20are%20strongly%20shaped%20by%20recurring%20external%20events%20such%20as%20holidays%2C%20promotional%20campaigns%2C%20and%20settlement%20cycles.%20In%20practice%2C%20however%2C%20anomaly%20detection%20systems%20often%20evaluate%20these%20series%20without%20explicitly%20modeling%20event%20context%2C%20leading%20to%20excessive%20false%20positives%20during%20predictable%20event-driven%20fluctuations.%20We%20propose%20a%20context-aware%20framework%20based%20on%20impact-driven</loc><changefreq>daily</changefreq><priority>0.00</priority></url>
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<url><loc>http://sns.qzone.qq.com/cgi-bin/qzshare/cgi_qzshare_onekey?url=https://www.amazon.science/publications/impact-driven-event-embeddings-for-context-aware-forecasting-and-anomaly-detection-in-financial-time-series&amp;title=Impact-driven event embeddings for context-aware forecasting and anomaly detection in financial time series&amp;summary=Financial transaction time series are strongly shaped by recurring external events such as holidays, promotional campaigns, and settlement cycles. In practice, however, anomaly detection systems often evaluate these series without explicitly modeling event context, leading to excessive false positives during predictable event-driven fluctuations. We propose a context-aware framework based on impact-driven</loc><changefreq>daily</changefreq><priority>0.00</priority></url>
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<url><loc>https://www.amazon.science/publications/mailto:?body=Why%20is%20this%20on%20my%20bill%3F%20Documentation-grounded%20rule%20learning%20for%20itemized%20bill%20explanation%0A%0Ahttps%3A%2F%2Fwww.amazon.science%2Fpublications%2Fwhy-is-this-on-my-bill-documentation-grounded-rule-learning-for-itemized-bill-explanation%0A%0AComplex%20itemized%20bills%20from%20services%20such%20as%20cloud%20computing%2C%20healthcare%2C%20or%20telecommunications%20can%20be%20difficult%20to%20understand%20because%20the%20pricing%20terms%20that%20determine%20what%20a%20customer%20pays%20are%20straightforward%2C%20but%20the%20eligibility%20rules%20that%20determine%20whether%20a%20given%20line%20item%2C%20a%20single%20charge%20for%20a%20product%20and%20quantity%2C%20qualifies%20for%20a%20given%20price%20are%20scattered%20across%20user%20guides%2C%20service%20terms%2C%20and%20fine</loc><changefreq>daily</changefreq><priority>0.00</priority></url>
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<url><loc>http://sns.qzone.qq.com/cgi-bin/qzshare/cgi_qzshare_onekey?url=https://www.amazon.science/publications/why-is-this-on-my-bill-documentation-grounded-rule-learning-for-itemized-bill-explanation&amp;title=Why is this on my bill? Documentation-grounded rule learning for itemized bill explanation&amp;summary=Complex itemized bills from services such as cloud computing, healthcare, or telecommunications can be difficult to understand because the pricing terms that determine what a customer pays are straightforward, but the eligibility rules that determine whether a given line item, a single charge for a product and quantity, qualifies for a given price are scattered across user guides, service terms, and fine</loc><changefreq>daily</changefreq><priority>0.00</priority></url>
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