Theory of Time Series AnalysisForecasting

Theory of Time Series Analysis/Forecasting, available at Free, has an average rating of 3.55, with 6 lectures, based on 28 reviews, and has 1112 subscribers.

You will learn about Theory of time series analysis Theory of time series forecasting Some methods to analyse a time series Some classical methods to forecast on a time series How to use machine learning to predict on a time series How to use Facebook Prophet to forecast a time series This course is ideal for individuals who are Beginner python developers who want to learn about time series analysis and forecasting It is particularly useful for Beginner python developers who want to learn about time series analysis and forecasting.

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Summary

Title: Theory of Time Series Analysis/Forecasting

Price: Free

Average Rating: 3.55

Number of Lectures: 6

Number of Published Lectures: 6

Number of Curriculum Items: 6

Number of Published Curriculum Objects: 6

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Theory of time series analysis

Theory of time series forecasting

Some methods to analyse a time series

Some classical methods to forecast on a time series

How to use machine learning to predict on a time series

How to use Facebook Prophet to forecast a time series

Who Should Attend

Beginner python developers who want to learn about time series analysis and forecasting

Target Audiences

Beginner python developers who want to learn about time series analysis and forecasting

In this course the student will learn the theory of time series analysis and forecasting.  Time series analysis is part of  artificial intelligence (AI) and is used by many companies to make predictions on sales, temperature, energy consumption, stock prices, etcetera.

Time series analysis involves looking at the time series and making judgements based on the look of the time series. The time series may need to be changed in an attempt to analyse it, and these changes could involve resampling or transforming in some fashion.

Time series forecasting involves making predictions on the time series. These predictions are based on past samples and it is important to use the correct forecasting method depending on the aoppearance of the time series, whether it has a trend or seasonality.

This course is broken down into six parts, being:-

1. Introduction

2. Time series analysis will discuss many methods used to analyse a time series, to include visualisation,smoothing, decomposition, stationarity, differencing, p-values, the Augmented Dickey Fuller test, handling missing values, the Granger causality test, autocorrelation, qqplot, lag, AIC, and RMSE.

3. Classical forecasting methods are discussed in this section, and includes the random walk, simple average, moving average, auto regression, VAR, VARMA, ARIMA, SARIMEX, SES, Holt method and Holt Winters method.

4. Machine learning and time series methods, with XGBoost and Random Forest being highlighted.

5. Facebook Prophet

6. Summary

Upon compeltion of this 45 minute course the student will be equipped with the necessary theoretical knowledge necessary to begin to code a time series analysis and forecast in the Python programming language.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Time series analysis

Lecture 1: Time series analysis

Chapter 3: Some classical time series forecasting methods

Lecture 1: Some classical forecasting methods

Chapter 4: Machine learning time series methods

Lecture 1: Machine learning time series methods

Chapter 5: How to use Facebook Prophet to forecast on a time series

Lecture 1: Facebook Prophet

Chapter 6: Summary

Lecture 1: Summary

Instructors

Tracy Renee
Data Scientist

Rating Distribution

1 stars: 2 votes

2 stars: 5 votes

3 stars: 7 votes

4 stars: 8 votes

5 stars: 6 votes

Frequently Asked Questions

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