Learn Time Series Analysis and Forecasting
Learn Time Series Analysis and Forecasting, available at $59.99, has an average rating of 4.75, with 33 lectures, based on 4 reviews, and has 80 subscribers.
You will learn about The student will be introduced to univariate time series analysys. The student will be introduced to multivariate time series analysis. The studen will be introduced to univariate time series forecasting. The stduent will be introduced to multivatiate time series forecasting. The student will be introduced to statsmodels, which is Python's statistical and time series library. The student will be introduced to Facebook Prophet, which is Facebook's open source time series forecasting library. The student will be introduced to a variety of time series forecasting models to explore. The student will be given the opportunity to undertake twelve time series forecasting projects. The student will be given the opportunity to enter two Kaggle competitions that concern time series forecasting. This course is ideal for individuals who are This course is suitable for people who would like to learn time series analysis and forecasting. It is particularly useful for This course is suitable for people who would like to learn time series analysis and forecasting.
Enroll now: Learn Time Series Analysis and Forecasting
Summary
Title: Learn Time Series Analysis and Forecasting
Price: $59.99
Average Rating: 4.75
Number of Lectures: 33
Number of Published Lectures: 33
Number of Curriculum Items: 33
Number of Published Curriculum Objects: 33
Original Price: £24.99
Quality Status: approved
Status: Live
What You Will Learn
- The student will be introduced to univariate time series analysys.
- The student will be introduced to multivariate time series analysis.
- The studen will be introduced to univariate time series forecasting.
- The stduent will be introduced to multivatiate time series forecasting.
- The student will be introduced to statsmodels, which is Python's statistical and time series library.
- The student will be introduced to Facebook Prophet, which is Facebook's open source time series forecasting library.
- The student will be introduced to a variety of time series forecasting models to explore.
- The student will be given the opportunity to undertake twelve time series forecasting projects.
- The student will be given the opportunity to enter two Kaggle competitions that concern time series forecasting.
Who Should Attend
- This course is suitable for people who would like to learn time series analysis and forecasting.
Target Audiences
- This course is suitable for people who would like to learn time series analysis and forecasting.
In the course, Learn Time Series Analysis and Forecasting, the student will be given an intensive overview of how to analyse and then make predictions on univariate and multivariate time series datasets.
The course is comprised of four sections, which are:-
1. Introduction to time series forecasting and analysis
2. Different time series methods to explore
3. Projects to work on
4. Kaggle competitions
The introduction to the course is comprised of five videos, covering ti me series analysis, statsmodels, and Facebook Prophet.
The different methods to be explored are comprised of:-
1. Baseline or lagged method
2. Holt Winters triple exponential smoothing method
3. Random walk method
4. Simple Average method
5. Moving average method
6. Auto Regression
7. ARIMA method
8. Simple exponential smoothing
9. Holt double exponential smoothing method
10. XGBoost Regressor
11. Random Forest method
12. Facebook Prophet
In addition to the classical time series forecasting methods, the course will cover how to predict on a time series dataset using machine learning and also Facebook Prophet, which is a newer library.
In the projects section of the course, the student will be given the code for a number of projects, which are:-
1. Tractor sales
2. Sickness at work
3. Waste collection
4. Exponential smoothing
5. Female births in California
6. Number of employees
7. Shampoo sales
8. SARIMEX
9. Energy consumption
10. An analysis of the spread of monkey pox
11. VAR
12. VARMA
In the final part of the course the student will be invited to enter a Kaggle competition relating to time series and the code for three past Kaggle competitions will be discussed.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Tutorial on time series analysis
Lecture 3: Tutorial on ARMA
Lecture 4: Tutorial on Facebook Prophet
Lecture 5: Example of a multivariate Facebook Prophet model
Chapter 2: Different models to explore
Lecture 1: Baseline lagged dataset
Lecture 2: Holt Winters (triple exponential smoothing)
Lecture 3: Random Walk
Lecture 4: Naive method
Lecture 5: Simple average method
Lecture 6: Moving average method
Lecture 7: Auto Regression
Lecture 8: ARIMA method
Lecture 9: Simple exponential smoothing
Lecture 10: Holt method (double exponential smoothing)
Lecture 11: XGBoost
Lecture 12: Random Forest
Lecture 13: FB Prophet
Chapter 3: Projects
Lecture 1: Tractor sales
Lecture 2: Sickness at work
Lecture 3: Waste collection
Lecture 4: Exponential smoothing
Lecture 5: Female births in California
Lecture 6: Number of employees
Lecture 7: Shampoo sales
Lecture 8: Sales (SARIMAX)
Lecture 9: Energy consumption
Lecture 10: Monkey pox
Lecture 11: Training (VAR)
Lecture 12: Waste collection (VARMA)
Chapter 4: Kaggle competitions
Lecture 1: Predict future sales
Lecture 2: March 2022 tabular competition
Lecture 3: Store sales
Instructors
-
Tracy Renee
Data Scientist
Rating Distribution
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- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 2 votes
- 5 stars: 2 votes
Frequently Asked Questions
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