Nature

Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition

In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear ...
ascopubs.org

ARIMA prediction model for mortality caused by metastatic cancers in the United States population up to the year 2050: A CDC WONDER Database analysis.

† For urbanization, AAMR 1999-2020. *P-value <0.05. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does not include a full text component.
Nature

Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability

Accurate demand forecasting is essential for informed decision-making in today’s dynamic business environment, where product demand often follows diverse and shifting patterns throughout increasingly ...
ascopubs.org

Predicting symptom complexity: Using autoregressive integrated moving average (ARIMA) models to create responsive clinic scheduling.

Impact of COVID19 pandemic on the hospitalization burden of cancer patients: Results of a quasi-experimental study. This is an ASCO Meeting Abstract from the 2021 ASCO Annual Meeting I. This abstract ...
International Monetary Fund

Jordan: Technical Assistance Report-Forecasting Framework for Currency in Circulation

The currency in circulation forecasting model presently used by the Central Bank of Jordan is aligned with international practices and provides a solid basis for liquidity management. The central bank ...
InfoWorld

Time series forecasting with ARMA and InfluxDB

Autoregressive moving average models have a number of advantages including simplicity. Here’s how to use an ARMA model with InfluxDB. An ARMA or autoregressive moving average model is a forecasting ...