Forecasting Wheat Production in Libya Using ARIMA Model-ARIMA
مجلة آفاق للدراسات الإنسانية والتطبيقيةThe wheat crop is a strategic crop in Libya as a food crop and a raw material for some food industries. The study aimed to predict the amount of wheat production in context of Libya during the next six years from 2023-2028. The Auto-regressive Integrated Moving Average (ARIMA) model has been used and relied on Food and Agriculture Organization data ...
openaire +1 more source
European Financial Management, EarlyView.ABSTRACT Climate change introduces new challenges for businesses which require them to find ways to be resilient. Green innovations contribute to boost Environmental, Social, and Governance (ESG)‐readiness leading to just transition without optimization.
Noman Arshed +4 more
wiley +1 more source
Verifikasi Model Arima Musiman Menggunakan Peta Kendali Moving Range (Studi Kasus : Kecepatan Rata-rata Angin Di Badan Meteorologi Klimatologi Dan Geofisika Stasiun Meteorologi Maritim Semarang) [PDF]
, 2014 Forecasting method Box-Jenkins ARIMA (Autoregressive Integrated Moving Average) is a forecasting method that can provide a more accurate forecasting results. To verify the model obtained using the one Moving Range Chart.
Azriati, K. F. (Kiki) +2 more
core
On Adjusting the One‐Sided Hodrick–Prescott Filter
Journal of Money, Credit and Banking, EarlyView.Abstract We show that one should not use the one‐sided Hodrick–Prescott (HP‐1s) filter as the real‐time version of the two‐sided HP (HP‐2s) filter: First, in terms of the extracted cyclical component, HP‐1s fails to remove low‐frequency fluctuations to the same extent as HP‐2s.
ELIAS WOLF +2 more
wiley +1 more source
PROPERTIES OF PREDICTORS IN OVERDIFFERENCED NEARLY NONSTATIONARY AUTOREGRESSION [PDF]
This paper analyzes the effect of overdifferencing a stationary AR(p+1) process whoselargest root is near unity. It is found that if the process is nearly nonstationary, the estimators ofthe overdifferenced model ARIMA (p, 1, 0) are root-T consistent. It
Daniel Peña, Ismael Sánchez
core
Time series forecasts of international tourism demand for Australia [PDF]
, 2001 This paper examines stationary and nonstationary time series by formally testing for the presence of unit roots and seasonal unit roots prior to estimation, model selection and forecasting.
Lim, Christine, MacAleer, Michael
core
Macroprudential Policy in the Euro Area
Journal of Money, Credit and Banking, EarlyView.Abstract This paper examines the development and impact of macroprudential policies in the euro area. We construct a novel index that captures the stance of macroprudential policy, and we highlight its main stylized facts since the inception of the euro in 1999. We combine a narrative approach and a structural VAR method to show that both unanticipated
ÁLVARO FERNÁNDEZ‐GALLARDO +1 more
wiley +1 more source
Financial Time Series Uncertainty: A Review of Probabilistic AI Applications
Journal of Economic Surveys, EarlyView.ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen +4 more
wiley +1 more source
Time series analysis and prediction of scarlet fever incidence trends in Jiangsu Province, China: using ARIMA and TBATS models [PDF]
, 2022 Zaijin Guo +4 more
openalex +1 more source
TEMPORAL AGGREGATION EFFECTS IN CHOOSING THE OPTIMAL LAG ORDER IN STABLE ARMA MODELS. SOME MONTE CARLO RESULTS. [PDF]
A crucial aspect of empirical research based on ARIMA(p,q) model is the choice of the appropriate lag order. Several criteria have been used in order to identify the appropriate order of a ARIMA(p,q) process.
Dikaios Tserkezos, Maria Nikoloudaki
core