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Time Series Analysis, Forecasting, and Machine Learning

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  • 11,895 Students
  • Updated 11/2025
4.8
(3,023 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
23 Hour(s) 27 Minute(s)
Language
English
Taught by
Lazy Programmer Team, Lazy Programmer Inc.
Rating
4.8
(3,023 Ratings)
5 views

Course Overview

Time Series Analysis, Forecasting, and Machine Learning

Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting

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

  • ARIMA, SARIMA, SARIMAX, and Auto ARIMA

  • ACF and PACF

  • Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)

  • Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)

  • Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)

  • GRUs and LSTMs for Time Series Forecasting

We will cover applications such as:

  • Time series forecasting of sales data

  • Time series forecasting of stock prices and stock returns

  • Time series classification of smartphone data to predict user behavior

The VIP version of the course will cover even more exciting topics, such as:

  • AWS Forecast (Amazon's state-of-the-art low-code forecasting API)

  • GARCH (financial volatility modeling)

  • FB Prophet (Facebook's time series library)

So what are you waiting for? 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

Course Content

  • 10 section(s)
  • 175 lecture(s)
  • Section 1 Welcome
  • Section 2 Getting Set Up
  • Section 3 Time Series Basics
  • Section 4 Exponential Smoothing and ETS Methods
  • Section 5 ARIMA
  • Section 6 Vector Autoregression (VAR, VMA, VARMA)
  • Section 7 Machine Learning Methods
  • Section 8 Deep Learning: Artificial Neural Networks (ANN)
  • Section 9 Deep Learning: Convolutional Neural Networks (CNN)
  • Section 10 Deep Learning: Recurrent Neural Networks (RNN)

What You’ll Learn

  • 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)


Reviews

  • R
    Robert Chun
    5.0

    Love the instructor

  • Y
    Yashaswi Prakash
    1.0

    I don't like this course. It is running at high speed and it is monotonous . It is definitely not worth the price being charged

  • H
    Harshad Parulekar
    5.0

    yes, it's very helpful.

  • K
    Konrad Borowiec
    1.0

    Ridiculous way of sharing the code snippets. Atrocious and senseless

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