Prerequisites for Deep Learning for Time Series

Direct prerequisites (4)

1. Time Series Foundationslayer 2, tier 2
2. State Space Modelslayer 2, tier 2
3. Recurrent Neural Networkslayer 3, tier 2
4. Transformer Architecturelayer 4, tier 2

Reachable through the chain (25)

These topics are not directly cited as prerequisites but are reached transitively by following the chain upward. Working through the direct prerequisites pulls these in.

• Kolmogorov Probability Axiomslayer 0A, tier 1
• Sets, Functions, and Relationslayer 0A, tier 1
• Basic Logic and Proof Techniqueslayer 0A, tier 2
• Expectation, Variance, Covariance, and Momentslayer 0A, tier 1
• Random Variableslayer 0A, tier 1
• Common Probability Distributionslayer 0A, tier 1
• Stochastic Processes for MLlayer 2, tier 2
• Measure-Theoretic Probabilitylayer 0B, tier 1
• Concentration Inequalitieslayer 1, tier 1
• Markov Chains and Steady Statelayer 1, tier 2
• Eigenvalues and Eigenvectorslayer 0A, tier 1
• Matrix Operations and Propertieslayer 0A, tier 1
• Kalman Filterlayer 2, tier 1
• Feedforward Networks and Backpropagationlayer 2, tier 1
• Differentiation in Rⁿlayer 0A, tier 1
• Vectors, Matrices, and Linear Mapslayer 0A, tier 1
• Continuity in Rⁿlayer 0A, tier 1
• Metric Spaces, Convergence, and Completenesslayer 0A, tier 1
• Matrix Calculuslayer 1, tier 1
• The Jacobian Matrixlayer 0A, tier 1
• The Hessian Matrixlayer 0A, tier 1
• Activation Functionslayer 1, tier 1
• Convex Optimization Basicslayer 1, tier 1
• Attention Mechanism Theorylayer 4, tier 2
• Softmax and Numerical Stabilitylayer 1, tier 1