دانلود کتاب Using Artificial Neural Networks for Timeseries Smoothing and Forecasting: Case Studies in Economics (Studies in Computational Intelligence, 979)

فهرست مطالب :

Introduction
Contents
1 Time Series and Their Importance to the Economy
1.1 Trend
1.2 Seasonality
1.3 Non-linearity
1.4 Conditioned Heteroscedasticity
References
2 Econometrics—Selected Models
2.1 Linear Models
2.2 Arima
2.2.1 Case
2.3 Exponential Smoothing
2.3.1 Case
2.4 Interrupted Time Series
2.5 Fourier Transformation
2.5.1 Case
2.6 Volatility Models
References
3 Artificial Neural Networks—Selected Models
3.1 Radial Basis Function Neural Networks (Explanation, Usage)
3.1.1 Mathematical Background
3.1.2 Case
3.2 Multi-Layer Perceptron Neural Networks
3.2.1 Mathematical Background
3.2.2 Case
3.3 Long Short Term Memory Neural Networks
3.3.1 Mathematical Background
3.3.2 Case
References
4 Comparison of Different Methods
4.1 Neural networks
4.1.1 Case
4.2 Decision Tree
4.2.1 Case
4.3 Gaussian Process
4.3.1 Case
4.4 Gradient Boosted Trees
4.4.1 Case
4.5 Linear Regression
4.5.1 Case
4.6 Nearest Neighbours
4.6.1 Case
4.7 Random Forest
4.7.1 Case
4.8 Mathematica: Comparison
References
5 Conclusion