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Computer Science
Data Science
Time Series Analysis and Forecasting
1. Introduction to Time Series Data
2. Mathematical Foundations
3. Fundamental Concepts
4. Data Preprocessing and Exploration
5. Classical Forecasting Models
6. Advanced Statistical Models
7. Machine Learning for Time Series
8. Model Evaluation and Validation
9. Advanced Topics and Applications
2.
Mathematical Foundations
2.1.
Basic Probability and Statistics Review
2.1.1.
Random variables
2.1.2.
Probability distributions
2.1.3.
Expected value and variance
2.1.4.
Covariance and correlation
2.2.
Stochastic Processes
2.2.1.
Definition of stochastic processes
2.2.2.
Discrete-time stochastic processes
2.2.3.
Continuous-time stochastic processes
2.2.4.
Sample paths and realizations
2.3.
White Noise Processes
2.3.1.
Definition and properties
2.3.2.
Gaussian white noise
2.3.3.
Applications in time series modeling
2.4.
Random Walk Processes
2.4.1.
Simple random walk
2.4.2.
Random walk with drift
2.4.3.
Properties and characteristics
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1. Introduction to Time Series Data
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3. Fundamental Concepts