Time Series Analysis and Forecasting using Python

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Satyajit Pattnaik

10:05:30

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1. Introduction

1. Introduction.html

2. Introduction to Time Series Forecasting

1. What is Time Series.mp4

04:29

2. Time Series vs Regression.mp4

10:40

3. What is Time Series Analysis.mp4

02:27

3. Understanding Time Series Data

1. What is Anomaly Detection.mp4

04:36

2. Components of Time Series.mp4

09:09

3. Time Series Decomposition.mp4

01:54

4. Implementation of Decomposition.mp4

06:36

5. Additive and Multiplicative Decompostion.mp4

08:41

6. Time Series Stationarity.mp4

08:54

7. Testing Time Series Staionarity.mp4

07:03

8. Transformation.mp4

06:10

4. 4 Preprocessing and Data Cleaning

1. Introduction to Pre-Processing.mp4

03:36

2. Handle Missing Value.mp4

06:25

3. Implementation of Handle Missing value in Python.mp4

11:35

4. Outlier Treatment.mp4

09:21

5. Sigma Technique (Standard Deviation).mp4

13:41

6. Feature Scaling.mp4

11:04

7. Feature Scaling Technique (Standardization).mp4

04:13

8. Feature Scaling Technique (Normalization).mp4

02:30

9. Implementation of Feature Scaling.mp4

13:50

10. Feature Encoding.mp4

12:25

11. Implementation of Feature Encoding.mp4

10:46

5. 5 Exploratory Data Analysis

1. Introduction.html

2. What is EDA.mp4

03:08

3. What is Visualization.mp4

04:49

4. Data Sourcing.mp4

04:35

5. Data Cleaning.mp4

04:11

6. Handling Missing Values (Theory).mp4

06:25

7. Handling Missing Values (Practicals).mp4

11:35

8. Outlier Treatment.mp4

09:21

9. Outlier Treatment (Practicals).mp4

13:41

10. Types of Analysis.mp4

02:35

11. Univariate Analysis.mp4

09:00

12. Bivariate Analysis.mp4

05:15

13. Multivariate Analysis.mp4

01:21

14. Numerical Analysis.mp4

03:56

15. Analysis (Practicals).mp4

30:14

16. Derived Metrics.mp4

04:33

17. Feature Binning (Theory).mp4

07:17

18. Feature Binning (Practicals).mp4

10:48

19. Feature Encoding (Theory).mp4

12:25

20. Feature Encoding (Practicals).mp4

10:46

6. 6 Time Series Forecasting Models A Comprehensive Overview

1. Algorithms.mp4

01:47

2. ARIMA [part 1].mp4

03:45

3. ARIMA [part 2].mp4

07:26

4. Auto Regressive Theory.mp4

08:37

5. Moving average Theory.mp4

09:40

6. Auto-Correlation Function (ACF) &Partical Auto-Correlation Function (PACF).mp4

13:37

7. Find PDQ.mp4

03:33

8. ARIMA [practicals 1].mp4

14:12

9. ARIMA [practicals 2].mp4

12:53

10. Implementation of ARIMA.mp4

10:06

11. Decompostion.mp4

03:26

12. Auto Correlation vs Partical Auto Correlation.mp4

03:38

13. Choosing the best transformation.mp4

10:16

14. Grid Search [part 1].mp4

08:49

15. Grid Search [part 2].mp4

02:04

16. Final Model.mp4

11:04

17. FBProphet [part 1].mp4

07:56

18. FBProphet [part 2].mp4

11:05

19. FBProphet [part 3].mp4

06:07

7. 7 Multivariate Time Series Forecasting Methods

1. Multi Variate TS Analysis.mp4

06:35

2. FB Prophet Uni & Multi Variate.mp4

13:33

8. 8 Evaluating Forecasting Performance

1. Introduction.mp4

05:12

2. Forecasting Evaluation Metrics.mp4

02:49

3. Mean Squarred Error.mp4

02:45

4. Root Mean Sqaured Error.mp4

02:12

5. Mean Absolute Percentage Error.mp4

04:47

9. 9 Time Series Forecasting in Practice Case Studies

1. Project 1 - Energy Demand Forecasting [part 1].mp4

03:10

2. Project 1 - Energy Demand Forecasting [part 2].mp4

06:26

3. Project 1 - Energy Demand Forecasting [part 3].mp4

07:04

4. Project 2 - Stock Market Prediction [part 1].mp4

04:42

5. Project 2 - Stock Market Prediction [part 2].mp4

04:07

6. Project 2 - Stock Market Prediction [part 3].mp4

13:48

7. Project 3 - Demand Forecasting [part 1].mp4

03:32

8. Project 3 - Demand Forecasting [part 2].mp4

13:32

9. Project 3 - Demand Forecasting [part 3].mp4

09:57

10. Project 3 - Demand Forecasting [part 4].mp4

01:30

11. Project 3 - Demand Forecasting [part 5].mp4

15:14

12. Project 3 - Demand Forecasting [part 6].mp4

08:34

Description

Learn about Time Series Analysis and Forecasting models using Python in just under 11 hours.

What You'll Learn?

Get a solid understanding of Time Series Analysis and Forecasting
Building different Time Series Forecasting Models in Python
Learn about different variants of ARIMA, Facebook Prophet & LSTM models for forecasting
3 Industry level projects
Understand the business scenarios where Time Series Analysis is applicable
Use Pandas DataFrames to manipulate Time Series data and make statistical computations

Who is this for?

Students need to have Python, if not, they can get started with Google Colab or any online IDEs.

Beginner level Machine Learning concepts will be helpful

What You Need to Know?

Basic knowledge on Regression topics

Python installation is needed, but in case you don't have, you can still learn via Google Colab

More details

Description
In this comprehensive Time Series Analysis and Forecasting course, you'll learn everything you need to confidently analyze time series data and make accurate predictions. Through a combination of theory and practical examples, in just 10-11 hours, you'll develop a strong foundation in time series concepts and gain hands-on experience with various models and techniques.

This course also includes Exploratory Data Analysis which might not be 100% applicable for Time Series Analysis &Â Forecasting, but these concepts are very much needed in the Data space!!

This course includes:
Understanding Time Series: Explore the fundamental concepts of time series analysis, including the different components of time series, such as trend, seasonality, and noise.
Decomposition Techniques: Learn how to decompose time series data into its individual components to better understand its underlying patterns and trends.
Autoregressive (AR) Models: Dive into autoregressive models and discover how they capture the relationship between an observation and a certain number of lagged observations.
Moving Average (MA) Models: Explore moving average models and understand how they can effectively smooth out noise and reveal hidden patterns in time series data.
ARIMA Models: Master the widely used ARIMA models, which combine the concepts of autoregressive and moving average models to handle both trend and seasonality in time series data.
Facebook Prophet: Get hands-on experience with Facebook Prophet, a powerful open-source time series forecasting tool, and learn how to leverage its capabilities to make accurate predictions.
Real-World Projects: Apply your knowledge and skills to three real-world projects, where you'll tackle various time series analysis and forecasting problems, gaining valuable experience and confidence along the way.
In addition to the objectives mentioned earlier, our course also covers the following topics:
Preprocessing and Data Cleaning: Students will learn how to preprocess and clean time series data to ensure its quality and suitability for analysis. This includes handling missing values, dealing with outliers, and performing data transformations.
Multivariate Forecasting: The course explores techniques for forecasting time series data that involve multiple variables. Students will learn how to handle and analyze datasets with multiple time series and understand the complexities and challenges associated with multivariate forecasting.
By the end of this course, you'll have a solid understanding of time series analysis and forecasting, as well as the ability to apply different models and techniques to solve real-world problems. Join us now and unlock the power of time series data to make informed predictions and drive business decisions. Enroll today and start your journey toward becoming a time series expert!
Who this course is for:
Students need to have Python, if not, they can get started with Google Colab or any online IDEs.
Beginner level Machine Learning concepts will be helpful

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A Data Scientist with a passion for turning data into actionable insights, and meaningful stories. Right from the data extraction till the final data product or actionable insights, I enjoy the journey with the data. 12+ years experience working in AI, Data Analytics and Data Sciences across different industry verticals including telecom, cyber security, insurance, e-commerce etc. Conducted various training sessions on Data Science, Analytics, ML & AI, as well as attended various International conferences as keynote speaker & guest on Data Science, ML & AI in various universities across APAC Region. Over 8+ years of training & teaching experience into Data Science & Analytics

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