Investigation of Social Media Metrics With Respect to Demand Modeling for Promotional Products
Journal of Forecasting, EarlyView.ABSTRACT Social media (SM) has revolutionized the way companies connect with customers, enabling more personalized marketing strategies and enhancing engagement. With platforms like Facebook offering detailed user data, businesses can create more targeted advertising campaigns. This paper proposes an approach to categorizing SM variables based on their
Yvonne Badulescu +3 more
wiley +1 more source
Seasonal Decomposition‐Enhanced Deep Learning Architecture for Probabilistic Forecasting
Journal of Forecasting, EarlyView.ABSTRACT Time series decomposition as a general preprocessing method has been widely used in the field of time series forecasting. However, since the future is unknown, this preprocessing practice is limited in realistic forecasting, especially in real‐time forecasting scenarios.
Keyan Jin +1 more
wiley +1 more source
Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models. [PDF]
BMC Public Health, 2023 Cheng C +7 more
europepmc +1 more source
Regional employment forecasts with spatial interdependencies [PDF]
"The labour-market policy-mix in Germany is increasingly being decided on a regional level. This requires additional knowledge about the regional development which (disaggregated) national forecasts cannot provide.
Hampel, Katharina +4 more
core
When Are Statistical Forecast Gains Economically Relevant? Evidence From Bitcoin Returns
Journal of Forecasting, EarlyView.ABSTRACT We study how statistical forecast gains for Bitcoin translate into trading profits. Using real‐time out‐of‐sample forecasts from daily bivariate VARs from October 2021 to February 2024, we show that Bitcoin returns are forecastable and that seven predictive indices yield significant gains in directional accuracy (DA).
Rehan Arain, Stephen Snudden
wiley +1 more source
Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands. [PDF]
PLoS One, 2023 Perez-Guerra UH +8 more
europepmc +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 +1 more source
On continuous-time autoregressive fractionally integrated moving average processes
Bernoulli, 2009 In this paper, we consider a continuous-time autoregressive fractionally integrated moving average (CARFIMA) model, which is defined as the stationary solution of a stochastic differential equation driven by a standard fractional Brownian motion. Like the discrete-time ARFIMA model, the CARFIMA model is useful for studying time series with short memory,
openaire +3 more sources
A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets
Journal of Forecasting, EarlyView.ABSTRACT This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying c$$ c $$ latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids.
Shafqat Iqbal, Štefan Lyócsa
wiley +1 more source
Interruption time series analysis using autoregressive integrated moving average model: evaluating the impact of COVID-19 on the epidemic trend of gonorrhea in China. [PDF]
BMC Public Health, 2023 Li Y +5 more
europepmc +1 more source