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 Pythons statistical and time series library. The student will be introduced to Facebook Prophet, which is Facebooks 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.

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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 Pythons statistical and time series library.

The student will be introduced to Facebook Prophet, which is Facebooks 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

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4 stars: 2 votes

5 stars: 2 votes

Frequently Asked Questions

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