Diogo Resende – Time Series Forecasting with Python
$42.00
Master the art of time series forecasting with Python to predict Airbnb demand using industry-leading models…
File Size: 1.6 GB.
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Description
Diogo Resende – Time Series Forecasting with Python
Overview
This project-based course will put you in the role of a Business Data Analyst at Airbnb tasked with predicting demand for Airbnb property bookings in New York. To accomplish this goal, you’ll use the Python programming language to build a powerful tool that utilizes the magic of time series forecasting.
- How to utilize the power of time series forecasting to predict the future
- How to use the four most relevant forecasting models used by Business Data Analysts today
- Practice the day-to-day skills needed for Business Data Analysis
- Build an impressive project to add to your portfolio to help you get hired
- Enhance your proficiency with Python, one of the most popular programming languages
Syllabus
- Introduction
- Course Introduction
- Exercise: Meet Your Classmates and Instructor
- Course Material
- Why Forecasting Matters
- Understanding Your Video Player (notes, video speed, subtitles + more)
- Set Your Learning Streak Goal
- Exploratory Data Analysis
- Game Plan
- TIme Series Data
- Case Study Briefing
- Python – Directory and Libraries
- Python – Loading the Data
- Python – Renaming Variable
- Python – Summary Statistics
- Additive vs. Multiplicative Seasonality
- Python – Seasonal Decomposition
- Python – Seasonal Graphs
- Python – Visualization – Basic Plot
- Python – Visualization – Customization
- Python – Visualization -Adding Events
- Python – Correlation
- Auto-Correlation Plots
- Python – Auto-Correlation Plot
- Python – Useful Commands Template
- Let’s Have Some Fun (+ Free Resources)
- (Facebook) Prophet
- Game Plan for Prophet
- Prophet and Structural Time Series
- Python – Preparing the Script
- Python – Prepare Date Variable
- Python – Easter Holiday
- Python – Remaining Holidays
- Python – Wrapping up the Events
- Prophet Parameters
- Python – Prophet Model
- Cross-Validation
- Python – Cross-Validation
- Assessing Forecasting
- Python – Cross-Validation Performance and Plotting
- Parameter Tuning
- Python – Parameter Grid
- Python – Parameter Tuning
- Python – Best Parameters and Exporting
- Python – Updating Useful Commands (Part 1)
- Python – Preparing Data Sets
- Python – Parameters and Final Model
- Python – Forecasting
- Python – Exporting Forecasts
- Python – Updating Useful Commands (Part 2)
- Pros and Cons
- Unlimited Updates
- SARIMAX
- SARIMAX Game Plan
- ARIMA
- Python – Preparing Script
- Auto-Regressive
- Integrated
- Python – Stationarity and Differencing
- Moving Average Component
- Optimization Factors
- Python – SARIMAX Model
- Python – Cross-Validation
- Python – Parameter Grid
- Python – Parameter Tuning
- Python – Exporting Best Parameters
- Python – Preparing the Script
- Python – Preparing Data
- Python – Tuned SARIMAX Model
- Python – Forecasting
- Python – Visualization and Export
- SARIMAX Pros and Cons
- Course Check-In
- How LinkedIn Silverkite Works
- LinkedIn Silverkite Game Plan
- LinkedIn Silverkite
- Silverkite vs. Prophet
- Python – Libraries and Data
- Python – Preparing Data
- Python – Metadata
- Silverkite Components
- Growth Terms
- Python – Growth Terms
- Seasonality Terms
- Python – Seasonality
- Python – Available Countries and Holidays
- Python – Holidays
- Python – Changepoints
- Python – Regressors
- Lagged Regressors
- Python – Lagged Regressors
- Python – Autoregression
- Fitting Algorithms Possibilities
- Ridge Regression
- XGBoost
- Boosting
- Feature Sampling
- Python – Custom Fit Algorithm
- Python – Silverkite Model
- Python – Cross-Validation Configuration
- Python – SIlverkite Parameter Tuning
- Python – Visualization and Preparing Results
- Python – Exporting Best Parameters
- Python – Preparing Script
- Python – Tuned Silverkite Model
- Python – Summary and Visualization
- Python – Forecasting and Exporting
- Pros and Cons
- Implement a New Life System
- Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM)
- Game Plan for LSTM
- Simple Neural Network
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM)
- Python – Directory and Libraries
- Python – Time Series Objects
- Python – Time Variables
- Python – Scaling
- LSTM Parameters
- Activation Functions
- Python – LSTM Model
- Python – Cross-Validation
- Python – Cross-Validation Performance
- Python – Parameter Grid
- Python – Parameter Tuning (Round 1)
- Python – Parameter Tuning (Round 2)
- Python – Changing from CPU to GPU
- Python – Parameter Tuning (Final Results)
- Python – Preparing Script
- Python – Tuned LSTM Model
- Python – Predictions and Exporting
- Pros and Cons
- Ensemble
- Ensemble Game Plan
- Ensemble Mechanism
- Python – Preparing Script and Loading Predictions
- Python – Loading Errors
- Python – Forecasting Weights
- Python – Ensemble Forecast and Visualization
- Ensemble Pros and Cons
- Where To Go From Here?
- Thank You!
- Review This Course!
- Become An Alumni
- Learning Guideline
- ZTM Events Every Month
- LinkedIn Endorsements
Taught by
Diogo Resende
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