Time Series Analysis And Forecasting Using Python

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

Requirements

Basic knowledge on Regression topics

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

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 & 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!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Introduction to Time Series Forecasting

Lecture 2 What is Time Series?

Lecture 3 Time Series vs Regression

Lecture 4 What is Time Series Analysis?

Section 3: Understanding Time Series Data

Lecture 5 What is Anomaly Detection?

Lecture 6 Components of Time Series

Lecture 7 Time Series Decomposition

Lecture 8 Implementation of Decomposition

Lecture 9 Additive and Multiplicative Decompostion

Lecture 10 Time Series Stationarity

Lecture 11 Testing Time Series Staionarity

Lecture 12 Transformation

Section 4: 4 Preprocessing and Data Cleaning

Lecture 13 Introduction to Pre-Processing

Lecture 14 Handle Missing Value

Lecture 15 Implementation of Handle Missing value in Python

Lecture 16 Outlier Treatment

Lecture 17 Sigma Technique (Standard Deviation)

Lecture 18 Feature Scaling

Lecture 19 Feature Scaling Technique (Standardization)

Lecture 20 Feature Scaling Technique (Normalization)

Lecture 21 Implementation of Feature Scaling

Lecture 22 Feature Encoding

Lecture 23 Implementation of Feature Encoding

Section 5: 5 Exploratory Data Analysis

Lecture 24 Introduction

Lecture 25 What is EDA

Lecture 26 What is Visualization

Lecture 27 Data Sourcing

Lecture 28 Data Cleaning

Lecture 29 Handling Missing Values (Theory)

Lecture 30 Handling Missing Values (Practicals)

Lecture 31 Outlier Treatment

Lecture 32 Outlier Treatment (Practicals)

Lecture 33 Types of Analysis

Lecture 34 Univariate Analysis

Lecture 35 Bivariate Analysis

Lecture 36 Multivariate Analysis

Lecture 37 Numerical Analysis

Lecture 38 Analysis (Practicals)

Lecture 39 Derived Metrics

Lecture 40 Feature Binning (Theory)

Lecture 41 Feature Binning (Practicals)

Lecture 42 Feature Encoding (Theory)

Lecture 43 Feature Encoding (Practicals)

Section 6: 6 Time Series Forecasting Models: A Comprehensive Overview

Lecture 44 Algorithms

Lecture 45 ARIMA [part 1]

Lecture 46 ARIMA [part 2]

Lecture 47 Auto Regressive Theory

Lecture 48 Moving average Theory

Lecture 49 Auto-Correlation Function (ACF) &Partical Auto-Correlation Function (PACF)

Lecture 50 Find PDQ

Lecture 51 ARIMA [practicals 1]

Lecture 52 ARIMA [practicals 2]

Lecture 53 Implementation of ARIMA

Lecture 54 Decompostion

Lecture 55 Auto Correlation vs Partical Auto Correlation

Lecture 56 Choosing the best transformation

Lecture 57 Grid Search [part 1]

Lecture 58 Grid Search [part 2]

Lecture 59 Final Model

Lecture 60 FBProphet [part 1]

Lecture 61 FBProphet [part 2]

Lecture 62 FBProphet [part 3]

Section 7: 7 Multivariate Time Series Forecasting Methods

Lecture 63 Multi Variate TS Analysis

Lecture 64 FB Prophet Uni & Multi Variate

Section 8: 8 Evaluating Forecasting Performance

Lecture 65 Introduction

Lecture 66 Forecasting Evaluation Metrics

Lecture 67 Mean Squarred Error

Lecture 68 Root Mean Sqaured Error

Lecture 69 Mean Absolute Percentage Error

Section 9: 9 Time Series Forecasting in Practice: Case Studies

Lecture 70 Project 1 - Energy Demand Forecasting [part 1]

Lecture 71 Project 1 - Energy Demand Forecasting [part 2]

Lecture 72 Project 1 - Energy Demand Forecasting [part 3]

Lecture 73 Project 2 - Stock Market Prediction [part 1]

Lecture 74 Project 2 - Stock Market Prediction [part 2]

Lecture 75 Project 2 - Stock Market Prediction [part 3]

Lecture 76 Project 3 - Demand Forecasting [part 1]

Lecture 77 Project 3 - Demand Forecasting [part 2]

Lecture 78 Project 3 - Demand Forecasting [part 3]

Lecture 79 Project 3 - Demand Forecasting [part 4]

Lecture 80 Project 3 - Demand Forecasting [part 5]

Lecture 81 Project 3 - Demand Forecasting [part 6]

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