On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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7 results for “SARIMAX”

Optimizing Sales Forecasts through Automated Integration of Market Indicatorsecon.EM

This study enhances sales forecasts by integrating market indicators into forecasting models.

problem Traditional forecasting models rely solely on historical demand data.

method Automated integration of macroeconomic time series data (GDP growth) into forecasting models using feature selection methods.

result Feature selection methods, especially Forward Feature Selection, significantly improve forecasting accuracy.

sales forecasting market indicators macroeconomic data feature selection Neural Prophet

2024-05-15abs ↗pdf ↗

Beyond the Hype: Comparing Lightweight and Deep Learning Models for Air Quality Forecastingcs.LG

Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.

problem Accurate forecasting of urban air pollution for public health and policy guidance.

method Investigated lightweight additive models (FBP, NP) vs. deep learning and hybrid pipelines on Beijing PM2.5 and PM10 data.

result Facebook Prophet consistently outperformed NeuralProphet and traditional models, achieving high

R^2

values.

air quality forecasting lightweight models Facebook Prophet NeuralProphet SARIMAX

2025-12-09abs ↗pdf ↗

MMformer with Adaptive Transferable Attention: Advancing Multivariate Time Series Forecasting for Environmental Applicationsstat.AP

MMformer improves forecasting of environmental time series data.

problem Accurately forecasting environmental change trends for policy-making.

method Meta-learning MTS model combining self-attention and adaptive transferable multi-head attention.

result MMformer outperforms other models in air quality and climate datasets, reducing prediction errors by 50% in MSE and 20% in MAE.

Environmental forecasting Meta-learning Time series analysis Air quality Climate change

2025-04-18abs ↗pdf ↗

Mitigating Financial Risk from Climate-Induced Agricultural Price Volatilitystat.AP

Study analyzes climate impact on agricultural prices, offering insurance solutions.

problem Financial risk from climate-induced agricultural price volatility.

method Historical and future climate projections, EGARCH and SARIMAX models, Black-Scholes framework.

result Improved agricultural risk modeling and insurance mechanisms.

agricultural price volatility climate change EGARCH model SARIMAX model Black-Scholes

2025-03-31abs ↗pdf ↗

Robust Short-Term OEE Forecasting in Industry 4.0 via Topological Data Analysisstat.AP

Study uses TDA to improve OEE forecasting in manufacturing.

problem Volatility and nonlinearity in OEE time series data.

method Topological Data Analysis (TDA) to transform raw data into structured knowledge.

result Improves OEE forecasting accuracy by at least 17%.

Industry 4.0 OEE forecasting Topological Data Analysis SARIMAX Manufacturing

2025-06-20abs ↗pdf ↗

Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimizationstat.ML

Hybrid model improves sequential data prediction by combining neural and time series models.

problem Nonlinear prediction in online settings with domain-specific feature engineering issues.

method Joint optimization of LSTM for feature extraction and SARIMAX for time series data using state space representations.

result Significant improvements in real-life competition datasets.

Sequential data Hybrid model Feature extraction Time series Joint optimization

2023-09-19abs ↗pdf ↗

Time series forecasting from partial observations via Non-negative Matrix Factorizationcs.LG

Paper introduces SMM for forecasting multiple time series with missing values.

problem Forecasting multiple time series with missing and noisy values.

method Sliding Mask Method (SMM) using Non-negative Matrix Factorization (NMF).

result The method outperforms state-of-the-art methods in time series forecasting.

time series forecasting non-negative matrix factorization missing data proximal alternating linearized method

2021-02-10abs ↗pdf ↗