[Stock Markets Analytics Zoomcamp] Module 3 "Modeling for Time-series data"

Links:
- Course Github: github.com/DataTalksClub/stoc...
- Registration Form: docs.google.com/forms/d/e/1FA...
- PythonInvest Website: pythoninvest.com/
- (support PythonInvest): buymeacoffee.com/pythoninvest
Timestamps:
[01:34] Module 3 Introduction
[02:08] Agenda for the session
[04:57] System Design View: Module 3 "The Model"
[06:42] Discussing questions from Slido and selected comments from Home Assignment 3 submissions
[18:57] Final Preparations for the Unified Dataset
[31:32] Categorical variables and "dummies" generation in Pandas
[35:31] Correlation Analysis between 200+ features and future positive growth (1 week, boolean)
[40:50] Temporal data split on Train, Validation, and Test sets
[45:35] Similarity of distributions for Train, Validation, Tests sets for a continuous (growth_future_5d) and discrete (is_positive_growth_5d) variables.
[52:20] Introducing Financial Time Series Modeling
[53:13] Manual rules as predictions ("naive" approach)
[59:53] Manual predictions quality on Test dataset
[1:13:33] Autoregressive Integrated Moving Average model (ARIMA) as an example of a statistical prediction for time series
[1:18:40] Binary model: Decision Tree Classifier from the scikit-learn library
[1:27:07] Explainability: obtaining the most important features from trained Decision Tree Classifiers of depth 10 and 20
[1:33:02] Inference on a Decision Tree Classifier and
[1:33:39] Recap
Module 3: Analytical Modeling for Time-Series data
- Framing Hypotheses and Unraveling Time-Series Predictions
- Heuristics and hand rules for practical predictions.
- Predicting time-series data: trends, seasonality, and remainder decomposition.
- Regression techniques for understanding data relationships.
- Binary classification to determine growth direction.
- [Optional] Example of neural networks in analytical modelling.

[@nickgoupinets]

Ivan, just wanted to say a huge thank you for all the work you put into this course. It's an unbelievable amount of information and an awesome way of getting started with the financial markets analytics for techies. Given a bit of ingenuity and looking carefully at the fundamentals combined with the power of predictive models is a sure way to beat the Wall street!

[@pythoninvest2480]

Thanks Nick! This is very rewarding to hear!