.... The data set was partitioned into training (22 months) and testing segments (11 months). The BetaSutte model, which explicitly decomposes time series into trend and residual components, was implemented alongside random forest, XGBoost, and ETS models. Performance was evaluated through multiple accuracy...

... volume Prediction Nonlinear autoregressive neural network Finance Forecasting Energy Time series Artificial intelligence Neural networks AR = autoregressive; AR-GARCH = autoregressive-generalized autoregressive conditional heteroskedasticity; ARIMA = autoregressive...

...Fatemeh Yazdani; Mehdi Khashei; Seyed Reza Hejazi Purpose This paper aims to detect the most profitable, i.e. optimal turning points (TPs), from the history of time series using a binary integer programming (BIP) model. TPs prediction problem is one of the most popular yet challenging topics...

... of turmoil and uncertainty. Figure 2 exhibits the time series plot of DJSIUS with the investor’s sentiment index popularly known as VIX. It is seen that the sustainability index and future stock market volatility are adversely associated. The equity market volatility uncertainty explains the rally...

... 07 11 2018 12 03 2019 08 06 2019 © Emerald Publishing Limited 2019 Emerald Publishing Limited Licensed re-use rights only Information systems Time series Artificial intelligence Modelling Decision analysis Neural networks Discrete wavelet transform Nonlinear...

... and heterogeneous behavior between groups. Fernando Rojas can be contacted at: fernando.rojas@uv.cl 09 03 2019 29 04 2019 26 06 2019 © Emerald Publishing Limited 2019 Emerald Publishing Limited Licensed re-use rights only Time series Inventory management Management...

... 2014 12 04 2014 18 11 2014 © Emerald Group Publishing Limited 2016 Emerald Group Publishing Limited Licensed re-use rights only Forecasting Time series Exchange rate Neural networks Back propagation algorithm Heteroscedasticity Figure 3. The tanh function...