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Time Series Analysis, Forecasting, and Machine Learning

Lazy Programmer Team and Lazy Programmer Inc.

Hello friends.

Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python.

Time Series Analysis has become an especially important field in recent years.

Read more

Hello friends.

Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python.

Time Series Analysis has become an especially important field in recent years.

  • With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.

  • COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.

  • Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.

Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.

We will cover techniques such as:

  • ETS and Exponential Smoothing

  • Holt's Linear Trend Model

  • Holt-Winters Model

  •  Signup now to get lifetime access, a certificate of completion you can show off on your LinkedIn profile, and the skills to use the latest time series analysis techniques that you cannot learn anywhere else.

    Thanks for reading, and I'll see you in class.

    UNIQUE FEATURES

    • Every line of code explained in detail - email me any time if you disagree

    • No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch

    • Not afraid of university-level math - get important details about algorithms that other courses leave out

Enroll now

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What's inside

Learning objectives

  • Ets and exponential smoothing models
  • Holt's linear trend model and holt-winters
  • Autoregressive and moving average models (arima)
  • Seasonal arima (sarima), and sarimax
  • Auto arima
  • The statsmodels python library
  • The pmdarima python library
  • Machine learning for time series forecasting
  • Deep learning (anns, cnns, rnns, and lstms) for time series forecasting
  • Tensorflow 2 for predicting stock prices and returns
  • Vector autoregression (var) and vector moving average (vma) models (varma)
  • Aws forecast (amazon's time series forecasting service)
  • Fb prophet (facebook's time series library)
  • Modeling and forecasting financial time series
  • Garch (volatility modeling)
  • Show more
  • Show less

Syllabus

Welcome
Introduction and Outline
Warmup (Optional)
Getting Set Up
Read more
Where to get the code, notebooks, and data
How to Succeed in This Course
Temporary 403 Errors
Time Series Basics
Time Series Basics Section Introduction
What is a Time Series?
Modeling vs. Predicting
Why Do We Care About Shapes?
Types of Tasks
Power, Log, and Box-Cox Transformations
Power, Log, and Box-Cox Transformations in Code
Forecasting Metrics
Financial Time Series Primer
Price Simulations in Code
Random Walks and the Random Walk Hypothesis
The Naive Forecast and the Importance of Baselines
Naive Forecast and Forecasting Metrics in Code
Time Series Basics Section Summary
Suggestion Box
Exponential Smoothing and ETS Methods
Exponential Smoothing Section Introduction
Exponential Smoothing Intuition for Beginners
SMA Theory
SMA Code
EWMA Theory
EWMA Code
SES Theory
SES Code
Holt's Linear Trend Model (Theory)
Holt's Linear Trend Model (Code)
Holt-Winters (Theory)
Holt-Winters (Code)
Walk-Forward Validation
Walk-Forward Validation in Code
Application: Sales Data
Application: Stock Predictions
SMA Application: COVID-19 Counting
SMA Application: Algorithmic Trading
Exponential Smoothing Section Summary
(Optional) More About State-Space Models
ARIMA
ARIMA Section Introduction
Autoregressive Models - AR(p)
Moving Average Models - MA(q)
ARIMA in Code
Stationarity
Stationarity in Code
ACF (Autocorrelation Function)
PACF (Partial Autocorrelation Function)
ACF and PACF in Code (pt 1)
ACF and PACF in Code (pt 2)
Auto ARIMA and SARIMAX
Model Selection, AIC and BIC
Auto ARIMA in Code
Auto ARIMA in Code (Stocks)
ACF and PACF for Stock Returns
Auto ARIMA in Code (Sales Data)
How to Forecast with ARIMA
Forecasting Out-Of-Sample
ARIMA Section Summary
Vector Autoregression (VAR, VMA, VARMA)
Vector Autoregression Section Introduction
VAR and VARMA Theory
VARMA Code (pt 1)
VARMA Code (pt 2)
VARMA Code (pt 3)
VARMA Econometrics Code (pt 1)
VARMA Econometrics Code (pt 2)
Granger Causality
Granger Causality Code
Converting Between Models (Optional)
Vector Autoregression Section Summary
Machine Learning Methods
Machine Learning Section Introduction
Supervised Machine Learning: Classification and Regression
Autoregressive Machine Learning Models
Machine Learning Algorithms: Linear Regression
Machine Learning Algorithms: Logistic Regression
Machine Learning Algorithms: Support Vector Machines
Machine Learning Algorithms: Random Forest
Extrapolation and Stock Prices
Machine Learning for Time Series Forecasting in Code (pt 1)
Forecasting with Differencing
Machine Learning for Time Series Forecasting in Code (pt 2)
Application: Predicting Stock Prices and Returns
Application: Predicting Stock Movements
Machine Learning Section Summary
Deep Learning: Artificial Neural Networks (ANN)
Artificial Neural Networks: Section Introduction
The Neuron
Forward Propagation
The Geometrical Picture
Activation Functions
Multiclass Classification

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores time series analysis, forecasting, and machine learning techniques relevant to fields like finance, healthcare, and business
Provides comprehensive coverage of topics ranging from ETS models to deep learning
Taught by experts in the field, with the instructors being the Lazy Programmer Team and Lazy Programmer Inc
Suitable for individuals looking to gain a deeper understanding of time series analysis and its applications
Requires familiarity with basic statistical concepts and Python programming, making it more suitable for experienced learners
Incorporates real-world examples and case studies, providing practical insights for learners

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Time Series Analysis, Forecasting, and Machine Learning with these activities:
Brush up on your Python programming skills
Ensures you have the necessary programming skills to work with time series data and models.
Browse courses on Python Programming
Show steps
  • Review the 'Python Basics' section of the course
  • Complete the 'Python Exercises' notebook
  • Work on a personal Python project
Join a study group or online forum
Provides opportunities to collaborate and exchange ideas with other students.
Show steps
  • Join the course discussion forum
  • Participate in weekly study group meetings
  • Discuss course concepts and share resources
Review foundational econometrics and time series analysis principles
Refreshes your understanding of the key theories and techniques used in time series analysis.
View Time Series Analysis With Applications in R on Amazon
Show steps
  • Read Chapters 1-4 of the book
  • Review the following topics: Stationarity, Autocorrelation, Partial autocorrelation, and Spectral density
  • Complete the practice problems at the end of each chapter
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Code-Along Practice: Data Structures and Algorithms in Python
Reinforce your grasp of essential data structures and algorithms through hands-on coding practice.
Browse courses on Data Structures
Show steps
  • Implement a stack and queue using Python.
  • Develop sorting algorithms (e.g., merge sort, quicksort).
  • Analyze the time complexity of various algorithms.
Dissecting Session: STL Components and Functionality
Deepen your understanding of the mechanics behind STL and its utilities.
Browse courses on STL
Show steps
  • Identify common STL components in Python.
  • Contrast the functionalities of vector, list, and map.
  • Examine memory management and iterator implementation specifics.
Practice building and validating time series models
Improves your ability to construct and evaluate time series models using Python.
Browse courses on Time Series Models
Show steps
  • Fork the course repository on GitHub
  • Complete the exercises in the 'Modeling' notebook
  • Validate your models using the metrics provided in the notebook
Explore advanced time series forecasting techniques
Expands your knowledge of modern time series forecasting techniques, including deep learning and machine learning.
Browse courses on Time Series Forecasting
Show steps
  • Watch the 'Advanced Forecasting Techniques' video lectures
  • Read the corresponding sections in the course book
  • Implement the techniques in the 'Advanced Forecasting' notebook
Create a time series forecasting app
Deepens your understanding of the practical applications of time series analysis.
Browse courses on Time Series Forecasting
Show steps
  • Choose a time series dataset of interest
  • Develop a time series forecasting model using the techniques learned in the course
  • Create a web or mobile app that allows users to interact with the model and make predictions
Write a white paper on a time series analysis project
Enhances your ability to communicate your ideas and findings clearly and effectively.
Browse courses on Time Series Analysis
Show steps
  • Choose a topic related to time series analysis that interests you
  • Conduct research and gather data
  • Analyze the data and draw conclusions
  • Write a white paper summarizing your project
Volunteer as a mentor for a beginner-level time series analysis course
Strengthens your understanding of the concepts by teaching and assisting others.
Show steps
  • Contact the instructor of a beginner-level time series analysis course
  • Offer your services as a volunteer mentor
  • Provide guidance and support to students

Career center

Learners who complete Time Series Analysis, Forecasting, and Machine Learning will develop knowledge and skills that may be useful to these careers:

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Effort
23.5 total hours
Via
Udemy
Instructors
Lazy Programmer Team
Lazy Programmer Inc.
Language
English
Good to know
Explores time series analysis, forecasting, and machine learning techniques relevant to fields like finance, healthcare, and business
Provides comprehensive coverage of topics ranging from ETS models to deep learning
Taught by experts in the field, with the instructors being the Lazy Programmer Team and Lazy Programmer Inc
Suitable for individuals looking to gain a deeper understanding of time series analysis and its applications
Requires familiarity with basic statistical concepts and Python programming, making it more suitable for experienced learners
Incorporates real-world examples and case studies, providing practical insights for learners
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