Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

Accurate forecasting of inter-urban traffic flow has been one of most important issues globally in the research on road traffic congestion. The information of inter-urban traffic with a cyclic data pattern presents a more challenging situation. This investigation presents a short-term traffic...

Prediction is a forecast of an event which may happen in future. Predictions are not necessary based upon the prior knowledge or experience for an event of interest. Every person does predictions but the quality of the predictions differs from person to person and that classifies them as a...

We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question....

Predictive AI is increasingly used to guide decisions on agents. I show that even a bias-neutral predictive AI can potentially amplify exogenous (human) bias in settings where the predictive AI represents a cost-adjusted precision gain to unbiased predictions, and the final judgments are made by...