Mape Morottaja
PulseAugur coverage of Mape Morottaja — every cluster mentioning Mape Morottaja across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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SpecLST framework enhances traffic forecasting with frequency-domain modeling
Researchers have developed SpecLST, a novel framework designed to improve traffic forecasting by efficiently modeling long-term dependencies and integrating diverse data sources. The system utilizes a fast Fourier trans…
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DSTNet: New AI Model for Causal Multi-Horizon Financial Forecasting
Researchers have developed DSTNet, a novel Dynamic Spectral Trajectory Network designed for causal multi-horizon financial forecasting. Unlike traditional wavelet-based methods, DSTNet retains the recent evolution of fi…
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New study uses simulator data to improve forecasting for new policies
A new study published on arXiv explores a method for improving forecasting models when a new decision policy is implemented. The research proposes using counterfactual data generated from simulators to train these model…
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New research paper details LLM-DRL integration for cloud continuum resource management
A new research paper proposes an extended taxonomy for managing resources across IoT, edge, and cloud environments using large language models (LLMs) and deep reinforcement learning (DRL). The proposed framework introdu…
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New AI model predicts train delays using Indian Railway Network data
Researchers have developed RSTGCN, a novel Graph Convolutional Network designed to predict average train delays at stations. This model incorporates train frequency-aware spatial attention and has been tested on a newly…
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Quantum physics approach enhances classical time series analysis
Researchers have developed QARIMA, a novel framework that applies quantum physics principles to classical time series analysis. This approach reformulates core ARIMA modeling components into quantum-compatible modules, …
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AI models evaluated for network utilization forecasting
A new research paper evaluates various AI and machine learning models for forecasting network utilization KPIs. The study compares traditional methods like seasonal decomposition and Prophet against machine learning alg…
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New framework improves retail demand forecasting with adaptive correction
Researchers have developed a new framework called Predict-then-Correct (PtC) to improve retail demand forecasting, particularly for situations with rapidly changing demand and limited early data. This framework combines…
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New method improves electricity load forecasting with deep learning
Researchers have developed a delta-based target reformulation method for short-term electricity load forecasting using deep learning models like LSTMs and Transformers. This approach predicts the change in load between …
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New SMO algorithm developed for epsilon-SVR with MAPE loss
Researchers have developed a new Sequential Minimal Optimization (SMO) algorithm for $\varepsilon$-SVR that incorporates Mean Absolute Percentage Error (MAPE) directly into the loss function. This novel approach modifie…
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TimingLLM predicts post-synthesis timing from Verilog with high accuracy
Researchers have developed TimingLLM, a novel two-stage framework designed to predict post-synthesis timing in Verilog code without requiring synthesis tools. The first stage employs a fine-tuned LLM to generate structu…