Results 71 to 80 of about 6,660,147 (290)

LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data

Journal of Hydroinformatics
The operation of smart wastewater treatment plants (WWTPs) is increasingly paramount in improving effluent quality, facilitating resource recovery and reducing carbon emissions.
Siddharth Seshan, Dirk Vries, Jasper Immink, Alex van der Helm, Johann Poinapen   +4 more
doaj   +1 more source

Modelling aboveground net primary production (ANPP) of an Atlantic mountain grassland based on time series approach

Cuadernos de Investigación Geográfica, 2019
Because primary productivity is related both with the energy that sustains food webs and with species diversity, it is usually considered a key ecosystem property and a reliable indicator of available forage.
A. Salaberria, G. García-Baquero, I. Odriozola, A. Aldezabal   +3 more
doaj   +1 more source

REGCMPNT : A Fortran Program for Regression Models with ARIMA Component Errors [PDF]

RegComponent models are time series models with linear regression mean functions and error terms that follow ARIMA (autoregressive-integrated-moving average) component time series models.
William R. Bell
core  

Parameter estimation of candidate ARIMA models.

, 2022
Parameter estimation of candidate ARIMA models.
Sizhang He (12126977), Daren Zhao (11271648), Zhiyi Wang (328693), Qing Cao (417020), Minghua Zhou (822341), Ruihua Zhang (2819011), Huiwu Zhang (7493942)   +6 more
core   +1 more source

A Novel Text‐Based Framework for Forecasting Carbon Prices

Journal of Forecasting, EarlyView.
ABSTRACT This study proposes a text‐based framework for predicting EU carbon prices. Using weekly data from 2020 to 2024, we construct a multivariate dataset combining financial indicators, commodity prices, Google Trends measures, and news‐based sentiment extracted using FinBERT.
Christian Oliver Ewald, Yaoyu Li
wiley   +1 more source

The decomposition of forecast in seasonal arima models. [PDF]

This paper presents a procedure to break down the forecast function of a seasonal ARIMA model in terms of its permanent and transitory components. Both depend on the initial values at the forecast origin, but their structures are fixed and independent of
Peña, Daniel, Espasa, Antoni
core  

Temporal Robustness in Cross‐Country Gross Domestic Product Forecasting: Evidence From Classical, Regime‐Switching, and Ensemble Models

Journal of Forecasting, EarlyView.
ABSTRACT This study evaluates whether the relative performance of gross domestic product forecasting models is temporally robust across economies and changing macroeconomic conditions. Using annual real gross domestic product data from the World Bank for 177 separately reported economies and territories, we conduct a common‐vintage pseudo‐out‐of‐sample
Achilleas Tampouris, Chaido Dritsaki
wiley   +1 more source

Forecast of the export of fresh strawberries from the Republic of Serbia using the ARIMA model [PDF]

Journal of Agricultural Sciences (Belgrade)
Regarding the export of fruit intended for fresh consumption, fresh strawberries are among the leading export products of the Republic of Serbia. The main export market for fresh strawberries is the Russian Federation, where an average of 8,447 tonnes of
Zdravković Vladimir D., Janković Šoja Svjetlana D., Vučković Dejana J.   +2 more
doaj   +1 more source

Beyond Prediction: Data, Baselines, Explanation, and Causation in Machine Learning for Food Insecurity

Food Safety and Health, EarlyView.
The gains from machine learning in nowcasting and forecasting food insecurity are still small and limited and cannot be observed in all countries. This review summarizes the public resources for data, corrects common misconceptions about model requirements, and establishes baseline requirements, explanations, causal inference, and equity in operational
Shabnam Mehboob, Azman Abdullah, Gunesh Kumar, Samreen Memon, Naila Afghan   +4 more
wiley   +1 more source

Missing observations and additive outliers in time series models [PDF]

, 1992
The paper deals with estimation of missing observations in possible nonstationary ARIMA models. First, the model is assumed known, and the structure of the interpolation filter is analyzed.
Maravall, Agustín, Peña, Daniel
core   +1 more source