Time Series Notes - Features Evaluation - Anamoly Detection

Time Series Features

• Date time features -  hour, month, and day of week for each observation. Daylight savings or not, Public holiday or not, Quarter of the year, Hour of day, Season of the year. 
• Lag features and window features - Business day, Quarter start, Weekly frequency
• Rolling window statistics - moving average
• Expanding window statistics - minimum, mean, and maximum values
• Domain-specific features - additional research into each feature and find out domain-specific information beyond what is provided in the dataset description
• Additional factors, such as trends, seasonality, holidays, and external economic variables.

Introduction to feature engineering for time series forecasting

Top 4 Time Series Feature Engineering Lessons From Kaggle

Time Series as Features

Ref - Link

A review on outlier/anomaly detection in time series data

An observation that deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism

Anomaly Detection Learning Resources

• Python Outlier Detection (PyOD)
• Python Streaming Anomaly Detection (PySAD)
• Python Graph Outlier Detection (PyGOD)

Anomaly detection techniques in time series data

Deep Learning for Anomaly Detection

Ref - Link

Keep Exploring!!!