August 01 2022

Learn Time Series Analysis and Forecasting


Learn Time Series Analysis and Forecasting
Learn Time Series Analysis and Forecasting
Published 08/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 33 lectures (8h 43m) | Size: 3.12 GB



And enter a Kaggle competition

What you'll 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.
Requirements
Python programming
Google Colab
Description
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.
Who this course is for
This course is suitable for people who would like to learn time series analysis and forecasting.
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