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Time Series Analysis, Forecasting, and Machine Learning

Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting
Instructor:
Lazy Programmer Team
5.536 estudiantes matriculados
English [Auto]
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)

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

Welcome

1
Introduction and Outline
2
Warmup (Optional)

Getting Set Up

1
Get Your Hands Dirty, Practical Coding Experience, Data Links
2
How to use Github & Extra Coding Tips (Optional)
3
Where to get the code, notebooks, and data
4
How to Succeed in This Course

Time Series Basics

1
Time Series Basics Section Introduction
2
What is a Time Series?
3
Modeling vs. Predicting
4
Why Do We Care About Shapes?
5
Types of Tasks
6
Power, Log, and Box-Cox Transformations
7
Power, Log, and Box-Cox Transformations in Code
8
Forecasting Metrics
9
Financial Time Series Primer
10
Price Simulations in Code
11
Random Walks and the Random Walk Hypothesis
12
The Naive Forecast and the Importance of Baselines
13
Naive Forecast and Forecasting Metrics in Code
14
Time Series Basics Section Summary
15
Suggestion Box

Exponential Smoothing and ETS Methods

1
Exponential Smoothing Section Introduction
2
Exponential Smoothing Intuition for Beginners
3
SMA Theory
4
SMA Code
5
EWMA Theory
6
EWMA Code
7
SES Theory
8
SES Code
9
Holt's Linear Trend Model (Theory)
10
Holt's Linear Trend Model (Code)
11
Holt-Winters (Theory)
12
Holt-Winters (Code)
13
Walk-Forward Validation
14
Walk-Forward Validation in Code
15
Application: Sales Data
16
Application: Stock Predictions
17
SMA Application: COVID-19 Counting
18
SMA Application: Algorithmic Trading
19
Exponential Smoothing Section Summary
20
(Optional) More About State-Space Models

ARIMA

1
ARIMA Section Introduction
2
Autoregressive Models - AR(p)
3
Moving Average Models - MA(q)
4
ARIMA
5
ARIMA in Code
6
Stationarity
7
Stationarity in Code
8
ACF (Autocorrelation Function)
9
PACF (Partial Autocorrelation Funtion)
10
ACF and PACF in Code (pt 1)
11
ACF and PACF in Code (pt 2)
12
Auto ARIMA and SARIMAX
13
Model Selection, AIC and BIC
14
Auto ARIMA in Code
15
Auto ARIMA in Code (Stocks)
16
ACF and PACF for Stock Returns
17
Auto ARIMA in Code (Sales Data)
18
How to Forecast with ARIMA
19
Forecasting Out-Of-Sample
20
ARIMA Section Summary

Vector Autoregression (VAR, VMA, VARMA)

1
Vector Autoregression Section Introduction
2
VAR and VARMA Theory
3
VARMA Code (pt 1)
4
VARMA Code (pt 2)
5
VARMA Code (pt 3)
6
VARMA Econometrics Code (pt 1)
7
VARMA Econometrics Code (pt 2)
8
Granger Causality
9
Granger Causality Code
10
Converting Between Models (Optional)
11
Vector Autoregression Section Summary

Machine Learning Methods

1
Machine Learning Section Introduction
2
Supervised Machine Learning: Classification and Regression
3
Autoregressive Machine Learning Models
4
Machine Learning Algorithms: Linear Regression
5
Machine Learning Algorithms: Logistic Regression
6
Machine Learning Algorithms: Support Vector Machines
7
Machine Learning Algorithms: Random Forest
8
Extrapolation and Stock Prices
9
Machine Learning for Time Series Forecasting in Code (pt 1)
10
Forecasting with Differencing
11
Machine Learning for Time Series Forecasting in Code (pt 2)
12
Application: Sales Data
13
Application: Predicting Stock Prices and Returns
14
Application: Predicting Stock Movements
15
Machine Learning Section Summary

Deep Learning: Artificial Neural Networks (ANN)

1
Artificial Neural Networks: Section Introduction
2
The Neuron
3
Forward Propagation
4
The Geometrical Picture
5
Activation Functions
4.8
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Incluye

23 horas de video a pedido
Acceso completo de por vida
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Certificado de finalización
Time Series Analysis, Forecasting, and Machine Learning
Precio:
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