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AI From Scratch/Phase 02/Lesson 15/~90 minutes

Time Series Fundamentals

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Past performance does predict future results -- if you check for stationarity first.

Back to phaseML Fundamentals Next lessonAnomaly Detection

Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Resources

Hyndman and Athanasopoulos, Forecasting: Principles and Practice (3rd ed.)Reading scikit-learn Time Series SplitReading statsmodels ARIMA docsReading Makridakis et al., The M5 Competition (2022)Reading Time Series AdvisorPrompt time_series.pyCode