Results 31 to 40 of about 6,660,147 (290)

Perspectiva histórica de los modelos ARIMA y su utilidad en el análisis económico [PDF]

, 1991
Este trabajo comienza enumerando las contribuciones a la teoría de procesos estocásticos estacionarios que aparecieron entre 1912 y 1942 y comentando, al mismo tiempo, los principales procedimientos existentes en las décadas de los cincuenta y sesenta ...
Espasa, Antoni
core   +1 more source

Forecasting Efficiency between State Space And ARIMA Models with Application [PDF]

المجلة العراقية للعلوم الاحصائية, 2008
This research shows the forecasting using both of ARIMA and state space models. We firstly predict using ARIMA models, then we predict again after using ARIMA state space representation.
doaj   +1 more source

Clustering Time Series Forecasting Model for Grouping Provinces in Indonesia Based on Granulated Sugar Prices

Journal of Applied Informatics and Computing
Clustering time series is the process of organizing data into groups based on similarities in specific patterns. This research uses the prices of granulated sugar in each province of Indonesia. According to USDA reports, sugar consumption in Indonesia in
Fida Fariha Amatullah, Erdanisa Aghnia Ilmani, Anwar Fitrianto, Erfiani Erfiani, L. M. Risman Dwi Jumansyah   +4 more
doaj   +1 more source

Estimated ARIMA-GARCH family models.

, 2022
Estimated ARIMA-GARCH family models.
Ruwan Jayathilaka (9557787), Presant Thevakumar (12884554)   +1 more
core   +1 more source

Sudden anaerobization in Amphibacillus xylanus increases intracellular labile ferrous iron and inhibits cell growth

FEBS Open Bio, EarlyView.
Abruptly changing from aerobic to anaerobic conditions (sudden anaerobization) induced growth inhibition and a significant increase in intracellular labile ferrous iron in the aerotolerant anaerobe Amphibacillus xylanus. We found that free flavins mediate efficient electron transfer from NADH to ferric iron under anaerobic conditions, suggesting that ...
Shinya Kimata, Yoichi Sakai, Keisuke Tanaka, Etsuro Yoshimura, Ken Kitano, Morio Ishikawa, Tadahiro Nakamoto, Tsutomu Takayama, Hirofumi Inoue, Kaho Nomura, Tomonori Suzuki, Masataka Uchino, Akira Abe, Youichi Niimura   +13 more
wiley   +1 more source

Comparison of EEMD-ARIMA, EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption

Journal of Hydroinformatics, 2022
Short-term (e.g., hourly) urban water consumption (or demand) prediction is of great significance for the optimal operation of the intelligent water distribution pump stations. In this study, three single models (autoregressive integrated moving average (
Xingpo Liu, Yiqing Zhang, Qichen Zhang
doaj   +1 more source

Forecasts with ARIMA Models

Freakonometrics, 2016
In our time series class this morning, I was discussing forecasts with ARIMA Models. Consider some simple stationnary AR(1) simulated time series n=95 set.seed(1) E=rnorm(n) X=rep(0,n) phi=.85 for(t in 2:n) X[t]=phi*X[t-1]+E[t] plot(X,type="l ...
Arthur Charpentier
openaire   +2 more sources

Forecasting Crime Using ARIMA Model

CoRR, 2020
Data mining is the process in which we extract the different patterns and useful Information from large dataset. According to London police, crimes are immediately increases from beginning of 2017 in different borough of London. No useful information is available for prevent crime on future basis.
Khawar Islam, Akhter Raza
openaire   +2 more sources

The Selection of ARIMA Models With or Without Regressors [PDF]

SSRN Electronic Journal, 2012
We develop a Cp statistic for the selection of regression models with stationary and nonstationary ARIMA error term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically Gaussian. We also prove that the distribution of the sum of squares of one step ahead standardized prediction errors ...
Johansen, Søren, Riani, Marco, Atkinson, Anthony C.   +2 more
openaire   +2 more sources

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source