ARM for Mathematical Modeling and Time Series Prediction Problems
ARM for mathematical modeling and time series prediction problems with code implementation insights
Random Forest Prediction for Time Series Analysis
The Random Forest algorithm can be effectively applied to time series forecasting, utilizing ensemble decision trees with code implementation considerations for temporal data handling.
GARCH Model Fitting
Applied GARCH model fitting to a time series dataset, including Augmented Dickey-Fuller (ADF) stationarity testing and volatility parameter optimization
Lorenz Time Series for Phase Space Reconstruction Analysis
Lorenz Time Series Dataset for Analyzing Dynamical Systems through Phase Space Reconstruction Techniques
Computation of Three-Dimensional and Two-Dimensional Biphase Spectrum Plots for Time Series Analysis
Calculate and visualize time series biphase spectrum using 3D and 2D plots, essential for investigating nonlinear processes with code-driven implementation techniques
Generating FARIMA Time Series Using the Definition Method
Generating FARIMA time series via definition method and estimating HURST parameters to produce time series with long-range dependence characteristics, with implementation insights on fractional differencing and parameter estimation algorithms.
Network Construction from Time Series Using Visibility Graph Theory
Constructing complex networks from time series through visibility graph theory and computing the clustering coefficient of the resulting network
Multifractal Characteristics of One-Dimensional Time Series
This function computes the multifractal characteristics of a one-dimensional time series, implementing algorithms for fractal dimension analysis across multiple scales.
Exponential Smoothing Method
Exponential Smoothing is one of the commonly used methods for time series forecasting, based on the theoretical foundation of trend extrapolation. It assumes that quantitative evolution characteristics of phenomena within a certain future period will not deviate from past development trends. The forecast value is a weighted sum of all previous observations, with different weights assigned to different data points - more recent data receives higher weights while older data receives lower weights. This method can be implemented using recursive formulas where the smoothing parameter α controls the weight decay rate.
MATLAB Time Series Analysis Source Code
MATLAB source code for time series analysis featuring comprehensive data processing, visualization functions, and statistical modeling algorithms for practical implementation