Theory of Time Series Analysis/Forecasting
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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