Arima
PulseAugur coverage of Arima — every cluster mentioning Arima across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
-
New framework uses LSTM for granular construction material price forecasting
A new research paper introduces a granular framework for forecasting construction material prices, utilizing the Construction Specifications Institute (CSI) MasterFormat structure. The framework integrates explanatory v…
-
MetaARIMA brings transfer learning to ARIMA time series forecasting
Researchers have developed MetaARIMA, a novel approach that applies transfer learning to the configuration of ARIMA models for time series forecasting. Unlike traditional methods that train each series independently, Me…
-
MetaARIMA brings transfer learning to classical time series forecasting
Researchers have developed MetaARIMA, a novel approach that applies transfer learning to classical ARIMA time series forecasting models. Unlike previous methods that configure models independently for each series, MetaA…
-
Mistral AI releases Timestral time-series models outperforming Google
Mistral AI has launched Timestral, a new family of four time-series foundation models, with the largest boasting 209 million parameters. These models achieve state-of-the-art performance on key benchmarks like GIFT-Eval…
-
Deep learning models outperform statistical methods in used electronics price forecasting
A new benchmark study systematically compares deep learning and statistical models for forecasting the resale prices of used electronics. Researchers found that the N-BEATS deep learning architecture significantly outpe…
-
Research questions effectiveness of hybrid AI models in financial forecasting
A new research paper titled "Signal Simplification Is Not Predictive Simplification: Diagnosing Residual Neural Forecasting in Short-Horizon Volatility" investigates the effectiveness of hybrid statistical-neural models…
-
LLMs generate synthetic manufacturing data, outperforming traditional methods
Researchers have developed a new method using Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. This approach addresses the common challenge of limited labeled data in manu…
-
Time Series Analysis and Forecasting Series Explores Models and Applications
This three-part series delves into time series analysis and forecasting, covering fundamentals, data preparation with Pandas, and various forecasting models. Part 3 focuses on modeling, exploring techniques like ARIMA, …
-
Open-source pipeline enables crypto data analysis and fraud detection
A new open-source pipeline has been developed to process cryptocurrency market data, enabling ingestion, forecasting, and fraud detection on commodity hardware. This system utilizes Apache Kafka and Apache Spark to repl…
-
AI framework enhances border control with real-time queue prediction
Researchers have developed a novel multi-modal AI framework designed to enhance border control systems through real-time queue prediction and management. This framework integrates diverse data sources, utilizing Long Sh…
-
New MLAMA method improves public health forecasting accuracy
Researchers have developed a new method called Machine Learning and ARIMA Model Averaging (MLAMA) to improve public health forecasting. This approach combines statistical ARIMA models with machine learning models like r…
-
AI models predict risky driving hotspots in Sydney using vehicle data
A new research paper explores the use of connected vehicle data to proactively identify and forecast risky driving hotspots in Greater Sydney, Australia. The study quantifies risky driving through thresholds for hard br…
-
Hybrid ML framework forecasts cattle weight gain in grazing systems
Researchers have developed a hybrid machine learning framework to forecast cattle weight gain and growth patterns in grazing systems. The framework integrates various sensing data, including live weight, demographics, a…
-
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, …
-
LLM agents enhance HFMD forecasting with auditable, context-aware predictions
A new research paper introduces a two-agent neuro-symbolic framework designed for more auditable and context-aware forecasting of Hand, Foot, and Mouth Disease (HFMD). This system integrates an LLM-based Event Interpret…
-
New ARDL model embeds fairness in retail pricing strategies
Researchers have developed a new methodology for dynamic pricing in retail that incorporates fairness constraints to balance profitability with consumer welfare. The approach uses a log-log Autoregressive Distributed La…
-
Study finds structural priors can hinder SciML models in forecasting
A new study published on arXiv investigates the effectiveness of Structural Priors in Scientific Machine Learning (SciML) methods, specifically when these priors do not align with the underlying data-generating process.…
-
Foundation models for time series forecasting: break-even analysis reveals when they pay off
A new analysis of foundation models for time series forecasting suggests that their deployment is not always justified. The study compared models like Chronos, Moirai, and Lag-Llama against traditional methods such as X…
-
ML framework forecasts agricultural price volatility in import-isolated markets
Researchers have developed a machine learning framework to forecast agricultural price volatility in import-isolated markets, specifically focusing on Sri Lanka. The study utilizes a comprehensive dataset combining reta…
-
AutoML framework forecasts wireless tech trends using AI and 127k abstracts
Researchers have developed an AutoML framework to forecast technological trends in wireless networks and mobile computing by analyzing scientific publications. The system integrates clustering, topic modeling, and time …