Results 211 to 220 of about 73,625 (339)

Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes. [PDF]

Int J Environ Res Public Health, 2021
Watson L, Qi S, DeIure A, Link C, Chmielewski L, Hildebrand A, Rawson K, Ruether D.   +7 more
europepmc   +1 more source

Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model (Preprint)

, 2020
Ramesh Singh, Meenu Rani, Akshaya Srikanth Bhagavathula, Ranjit Sah, Alfonso J. Rodríguez‐Morales, Himangshu Kalita, Chintan Nanda, Shashi Sharma, Yagya Datt Sharma, Ali A. Rabaan, Jamal Rahmani, Pavan Kumar   +11 more
openalex   +1 more source

Gold Price Forecasting Using Autoregressive Integrated Moving Average (ARIMA) Method

, 2021
Ruth Elizabeth, Syahriol Sitorus
openalex   +2 more sources

Simulating climate change scenarios established by the Intergovernmental Panel on Climate Change in aquatic mesocosms

Limnology and Oceanography: Methods, EarlyView.
Abstract Climate change poses a major threat to ecosystems worldwide, with temperature changes being a key driver of ecological disruption in aquatic environments due to the limited ability of many aquatic species to relocate in response. Despite advances in aquatic experimental approaches to temperature control, most studies rely on simplified warming
Tauany Rodrigues, Aldo Caccavo, Saulo Jacques, Luiza O. Costa, Vinicius F. Farjalla, Aliny P. F. Pires   +5 more
wiley   +1 more source

A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China. [PDF]

PLoS One
Peng PY, Duan HY, Xu L, Sun JQ, Ma LJ, Zu Y, Yan TL.   +6 more
europepmc   +1 more source

Analyzing and Forecasting Laboratory Energy Consumption Patterns Using Autoregressive Integrated Moving Average Models [PDF]

Yitong Niu, Xiongjie Jia, Chee Keong Lee, Haoran Jiang, Cheu Peng Leh   +4 more
openalex   +1 more source

Systematic Literature Review on An Integrated Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Model with Heteroscedastic Error and Kriging Method for Forecasting Climate

, 2023
Putri Monika, Budi Nurani Ruchjana, Atje Setiawan Abdullah, Rahmat Budiarto   +3 more
openalex   +2 more sources

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Macromolecular Rapid Communications, EarlyView.
The performance of light‐emitting polymers emerges from coupled effects of chemical diversity, morphology, and exciton dynamics across multiple length scales. This Perspective reviews recent design strategies and experimental challenges, and discusses how machine learning can unify descriptors, data, and modeling approaches to efficiently navigate ...
Tian Tian, Yinyin Bao
wiley   +1 more source

Projection of age-standardised mortality rates for 23 cancer types in India: a nationwide projection until 2030 using autoregressive integrated moving average (ARIMA) models. [PDF]

BMJ Open
Shaji A, Narassima MS, Pavithran K, Vijaykumar DK, Verma V, John D.   +5 more
europepmc   +1 more source

The effects of single versus successive warm summers on an intertidal community

Oikos, EarlyView.
To accurately predict how organisms and ecological communities will respond to future conditions caused by climate change, we must consider the temporal dynamics of environmental stressors, including the effects of repeated exposures to stress. We performed a two‐year passive thermal manipulation in coastal British Columbia, Canada to determine how ...
Amelia V. Hesketh, Cassandra A. Konecny, Sandra M. Emry, Christopher D. G. Harley   +3 more
wiley   +1 more source