Wrapping-up
Python ile Zaman Serisi Verileri için Machine Learning
Chris Holdgraf
Fellow, Berkeley Institute for Data Science
Timeseries and machine learning
- The many applications of time series + machine learning
- Always visualize your data first
- The scikit-learn API standardizes this process
Feature extraction and classification
- Summary statistics for time series classification
- Combining multiple features into a single input matrix
- Feature extraction for time series data
Model fitting and improving data quality
- Time series features for regression
- Generating predictions over time
- Cleaning and improving time series data
Validating and assessing our model performance
- Cross-validation with time series data (don't shuffle the data!)
- Time series stationarity
- Assessing model coefficient and score stability
Advanced concepts in time series
- Advanced window functions
- Signal processing and filtering details
- Spectral analysis
Advanced machine learning
- Advanced time series feature extraction (e.g.,
tsfresh) - More complex model architectures for regression and classification
- Production-ready pipelines for time series analysis
Ways to practice
- There are a lot of opportunities to practice your skills with time series data.
- Kaggle has a number of time series predictions challenges
- Quantopian is also useful for learning and using predictive models others have built.
Let's practice!
Python ile Zaman Serisi Verileri için Machine Learning
