Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and clustering techniques. [PDF]
Heliyon, 2023 Ilu SY, Prasad R.
europepmc +1 more source
Forecasting House Prices: The Role of Market Interconnectedness
Journal of Forecasting, EarlyView.ABSTRACT While the existing research uncovers interconnections between various housing markets, it largely ignores the question of whether such linkages can improve house price predictions. To address this issue, we proceed in two steps. First, we forecast disaggregated house price growth rates from Australia and China to determine whether ...
Zac Chen +3 more
wiley +1 more source
A Hybrid Approach Based on Seasonal Autoregressive Integrated Moving Average and Neural Network Autoregressive Models to Predict Scorpion Sting Incidence in El Oued Province, Algeria, From 2005 to 2020. [PDF]
J Res Health Sci, 2023 Zenia S, L'Hadj M, Selmane S.
europepmc +1 more source
Forecasting Inflation in Developing Nations: The Case of Pakistan [PDF]
This study attempts to outline the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Pakistan’s inflation. A framework for ARIMA forecasting is drawn up. On the basis of
Feridun, Mete
core +1 more source
Forecasting GDP of Nepal using Autoregressive Integrated Moving Average (ARIMA) Model
, 2023 Hari Prasad Upadhyay, Bijay Lal Pradhan
openalex +2 more sources
Journal of Forecasting, EarlyView.ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das +2 more
wiley +1 more source
Modeling and Forecasting Stochastic Seasonality: Are Seasonal Autoregressive Integrated Moving Average Models Always the Best Choice? [PDF]
Evangelos E. Ioannidis +1 more
openalex +1 more source
Intraday Functional PCA Forecasting of Cryptocurrency Returns
Journal of Forecasting, EarlyView.ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley +1 more source