Time Series Analysis and Forecasting using Python

سرفصل های دوره

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

1. Introduction

1. Introduction.html

2. Introduction to Time Series Forecasting

1. What is Time Series

2. Time Series vs Regression

3. What is Time Series Analysis

3. Understanding Time Series Data

1. What is Anomaly Detection

2. Components of Time Series

3. Time Series Decomposition

4. Implementation of Decomposition

5. Additive and Multiplicative Decompostion

6. Time Series Stationarity

7. Testing Time Series Staionarity

8. Transformation

4. 4 Preprocessing and Data Cleaning

1. Introduction to Pre-Processing

2. Handle Missing Value

3. Implementation of Handle Missing value in Python

4. Outlier Treatment

5. Sigma Technique (Standard Deviation)

6. Feature Scaling

7. Feature Scaling Technique (Standardization)

8. Feature Scaling Technique (Normalization)

9. Implementation of Feature Scaling

10. Feature Encoding

11. Implementation of Feature Encoding

5. 5 Exploratory Data Analysis

1. Introduction.html

2. What is EDA

3. What is Visualization

4. Data Sourcing

5. Data Cleaning

6. Handling Missing Values (Theory)

7. Handling Missing Values (Practicals)

8. Outlier Treatment

9. Outlier Treatment (Practicals)

10. Types of Analysis

11. Univariate Analysis

12. Bivariate Analysis

13. Multivariate Analysis

14. Numerical Analysis

15. Analysis (Practicals)

16. Derived Metrics

17. Feature Binning (Theory)

18. Feature Binning (Practicals)

19. Feature Encoding (Theory)

20. Feature Encoding (Practicals)

6. 6 Time Series Forecasting Models A Comprehensive Overview

1. Algorithms

2. ARIMA [part 1]

3. ARIMA [part 2]

4. Auto Regressive Theory

5. Moving average Theory

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

7. Find PDQ

8. ARIMA [practicals 1]

9. ARIMA [practicals 2]

10. Implementation of ARIMA

11. Decompostion

12. Auto Correlation vs Partical Auto Correlation

13. Choosing the best transformation

14. Grid Search [part 1]

15. Grid Search [part 2]

16. Final Model

17. FBProphet [part 1]

18. FBProphet [part 2]

19. FBProphet [part 3]

7. 7 Multivariate Time Series Forecasting Methods

1. Multi Variate TS Analysis

2. FB Prophet Uni & Multi Variate

8. 8 Evaluating Forecasting Performance

1. Introduction

2. Forecasting Evaluation Metrics

3. Mean Squarred Error

4. Root Mean Sqaured Error

5. Mean Absolute Percentage Error

9. 9 Time Series Forecasting in Practice Case Studies

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

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

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

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

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

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

7. Project 3 - Demand Forecasting [part 1]

8. Project 3 - Demand Forecasting [part 2]

9. Project 3 - Demand Forecasting [part 3]

10. Project 3 - Demand Forecasting [part 4]

11. Project 3 - Demand Forecasting [part 5]

12. Project 3 - Demand Forecasting [part 6]

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