Learn Time Series Analysis and Forecasting

[LeeAndro]

Published 08/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English + srt | Duration: 33 lectures (8h 43m) | Size: 3.12 GB

The student will be introduced to univariate series analysys.​

The student will be introduced to multivariate series analysis.
The studen will be introduced to univariate series forecasting.
The stduent will be introduced to multivatiate series forecasting.
The student will be introduced to statsmodels, which is Python's statistical and series library.
The student will be introduced to Facebook Prophet, which is Facebook's open source series forecasting library.
The student will be introduced to a variety of series forecasting models to explore.
The student will be given the opportunity to undertake twelve series forecasting projects.
The student will be given the opportunity to enter two Kaggle competitions that concern series forecasting.

Python programming
Google Colab

In the course, Learn Series Analysis and Forecasting, the student will be given an intensive overview of how to analyse and then make predictions on univariate and multivariate series datasets.

The course is comprised of four sections, which are:-

1. Introduction to series forecasting and analysis

2. Different 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 series forecasting methods, the course will cover how to predict on a 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. SAX

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 series and the code for three past Kaggle competitions will be discussed.

This course is suitable for people who would like to learn series analysis and forecasting.

HomePage:

https://anonymz.com/https://www.udemy.com/course/learnseries-analysis-and-forecasting/

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