Time series forecasting using functional partial least square regression with stochastic volatility, GARCH, and exponential smoothing

Mathematics

• Time Series Forecasting 100%
• Exponential Smoothing 97%
• Conditional Heteroskedasticity 95%
• Partial Least Squares Regression 88%
• Stochastic Volatility 87%
• Forecasting 48%
• Functional Data Analysis 42%
• Standard Model 35%
• Trace 25%
• Volatility 24%
• Auxiliary Variables 23%
• Model 20%
• Forecast Evaluation 20%
• Currency 15%
• Methodology 15%
• Exchange rate 14%
• Principal Component Regression 14%
• Financial Time Series 14%
• Performance Evaluation 14%
• Partial Least Squares 13%
• Multicollinearity 13%
• Shrinkage 11%
• Market 10%
• Predict 9%
• Economics 9%
• High-dimensional 8%
• Regression 8%
• Standards 5%
• Operator 4%

Business & Economics

• Generalized Autoregressive Conditional Heteroskedasticity 79%
• Exponential Smoothing 78%
• Time Series Forecasting 77%
• Stochastic Volatility 69%
• Partial Least Squares 57%
• Predictive Ability 31%
• Return Volatility 20%
• Crude Oil 18%
• Performance Evaluation 13%
• Reality Check 13%
• Data Snooping 13%
• Exchange Rates 12%
• Shrinkage 11%
• Multicollinearity 11%
• Forecast Performance 11%
• Predictive Accuracy 11%
• Principal Components 10%
• Financial Time Series 10%
• Crude Oil Price 10%
• Financial Economics 9%
• Market Data 9%
• Methodology 9%
• Currency 9%
• Operator 8%
• Empirical Results 6%

Engineering & Materials Science

• Time series 55%
• Crude oil price 10%
• Economics 4%