## Time Series Analysis and Forecasting using Python

### What you’ll learn

Get a solid understanding of Time Series Analysis and Forecasting

Understand the business scenarios where Time Series Analysis is applicable

Building 5 different Time Series Forecasting Models in Python

Learn about Auto regression and Moving average Models

Learn about ARIMA and SARIMA models for forecasting

Use Pandas DataFrames to manipulate Time Series data and make statistical computations

### Requirements

Students will need to install Python and Anaconda software but we have a separate lecture to help you install the sameStudents will need to install Python and Anaconda software but we have a separate lecture to help you install the same

### Description

You’re looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right?You’ve found the right Time Series Forecasting and Time Series Analysis course using Python Time Series techniques. This course teaches you everything you need to know about different time series forecasting and time series analysis models and how to implement these models in Python time series.After completing this course you will be able to:Implement time series forecasting and time series analysis models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.Implement multivariate time series forecasting models based on Linear regression and Neural Networks.Confidently practice, discuss and understand different time series forecasting, time series analysis models and Python time series techniques used by organizationsHow will this course help you?A Verifiable Certificate of Completion is presented to all students who undertake this Time Series Forecasting course on time series analysis and Python time series applications.If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it. You will also learn time series forecasting models, time series analysis and Python time series techniques.Why should you choose this course?We believe in teaching by example. This course is no exception. Every Section’s primary focus is to teach you the concepts through how-to examples. Each section has the following components:Theoretical concepts and use cases of different forecasting models, time series forecasting and time series analysisStep-by-step instructions on implement time series forecasting models in PythonDownloadable Code files containing data and solutions used in each lecture on time series forecasting, time series analysis and Python time series techniquesClass notes and assignments to revise and practice the concepts on time series forecasting, time series analysis and Python time series techniquesThe practical classes where we create the model for each of these strategies is something which differentiates this course from any other available online course on time series forecasting, time series analysis and Python time series techniques..What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this course. They also have an in-depth knowledge on time series forecasting, time series analysis and Python time series techniques.We are also the creators of some of the most popular online courses – with over 170,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman – JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. – DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on time series forecasting, time series analysis and Python time series techniques.Each section contains a practice assignment for you to practically implement your learning on time series forecasting, time series analysis and Python time series techniques.What is covered in this course?Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will deal with time series forecasting, time series analysis and Python time series techniques. We will also explore how one can use forecasting models toSee patterns in time series dataMake forecasts based on modelsLet me give you a brief overview of the courseSection 1 – IntroductionIn this section we will learn about the course structure and how the concepts on time series forecasting, time series analysis and Python time series techniques will be taught in this course.Section 2 – Python basicsThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it’ll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.The basics taught in this part will be fundamental in learning time series forecasting, time series analysis and Python time series techniques on later part of this course.Section 3 – Basics of Time Series DataIn this section, we will discuss about the basics of time series data, application of time series forecasting, and the standard process followed to build a forecasting model, time series forecasting, time series analysis and Python time series techniques.Section 4 – Pre-processing Time Series DataIn this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models and execute time series forecasting, time series analysis and implement Python time series techniques.Section 5 – Getting Data Ready for Regression ModelIn this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.Section 6 – Forecasting using Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results.Section 7 – Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.Section 8 – Creating Regression and Classification ANN model in PythonIn this part you will learn how to create ANN models in Python.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.I am pretty confident that the course will give you the necessary knowledge and skills related to time series forecasting, time series analysis and Python time series techniques to immediately see practical benefits in your work place.Go ahead and click the enroll button, and I’ll see you in lesson 1 of this course on time series forecasting, time series analysis and Python time series techniques!CheersStart-Tech Academy

### Overview

Section 1: Introduction

Lecture 1 Welcome to the course

Lecture 2 What is Time Series Forecasting?

Lecture 3 Course Resources

Lecture 4 This is a milestone!

Section 2: Time Series – Basics

Lecture 5 Time Series Forecasting – Use cases

Lecture 6 Forecasting model creation – Steps

Lecture 7 Forecasting model creation – Steps 1 (Goal)

Lecture 8 Time Series – Basic Notations

Section 3: Setting up Python and Python Crash Course

Lecture 9 Installing Python and Anaconda

Lecture 10 Course resources

Lecture 11 Opening Jupyter Notebook

Lecture 12 Introduction to Jupyter

Lecture 13 Arithmetic operators in Python: Python Basics

Lecture 14 Strings in Python: Python Basics

Lecture 15 Lists, Tuples and Directories: Python Basics

Lecture 16 Working with Numpy Library of Python

Lecture 17 Working with Pandas Library of Python

Lecture 18 Working with Seaborn Library of Python

Section 4: Time Series – Data Loading

Lecture 19 Data Loading in Python

Section 5: Time Series – Feature Engineering

Lecture 20 Time Series – Feature Engineering Basics

Lecture 21 Time Series – Feature Engineering in Python

Section 6: Time Series – Resampling

Lecture 22 Time Series – Upsampling and Downsampling

Lecture 23 Time Series – Upsampling and Downsampling in Python

Section 7: Time Series – Visualization

Lecture 24 Time Series – Visualization Basics

Lecture 25 Time Series – Visualization in Python

Section 8: Time Series – Transformation

Lecture 26 Time Series – Power Transformation

Lecture 27 Moving Average

Lecture 28 Exponential Smoothing

Section 9: Time Series – Important Concepts

Lecture 29 White Noise

Lecture 30 Random Walk

Lecture 31 Decomposing Time Series in Python

Lecture 32 Differencing

Lecture 33 Differencing in Python

Section 10: Time Series – Test Train Split

Lecture 34 Test Train Split in Python

Section 11: Time Series – Naive (Persistence) model

Lecture 35 Naive (Persistence) model in Python

Section 12: Time Series – Auto Regression Model

Lecture 36 Auto Regression Model – Basics

Lecture 37 Auto Regression Model creation in Python

Lecture 38 Auto Regression with Walk Forward validation in Python

Section 13: Time Series – Moving Average model

Lecture 39 Moving Average model -Basics

Lecture 40 Moving Average model in Python

Section 14: Time Series – ARIMA model

Lecture 41 ACF and PACF

Lecture 42 ARIMA model – Basics

Lecture 43 ARIMA model in Python

Lecture 44 ARIMA model with Walk Forward Validation in Python

Section 15: Time Series – SARIMA model

Lecture 45 SARIMA model

Lecture 46 SARIMA model in Python

Section 16: Stationary time Series

Lecture 47 Stationary Time Series

Section 17: Linear Regression – Data Preprocessing

Lecture 48 Introduction

Lecture 49 Additional Course Resources

Lecture 50 Gathering Business Knowledge

Lecture 51 Data Exploration

Lecture 52 The Dataset and the Data Dictionary

Lecture 53 Importing Data in Python

Lecture 54 Univariate analysis and EDD

Lecture 55 EDD in Python

Lecture 56 Outlier Treatment

Lecture 57 Outlier Treatment in Python

Lecture 58 Missing Value Imputation

Lecture 59 Missing Value Imputation in Python

Lecture 60 Seasonality in Data

Lecture 61 Bi-variate analysis and Variable transformation

Lecture 62 Variable transformation and deletion in Python

Lecture 63 Non-usable variables

Lecture 64 Dummy variable creation: Handling qualitative data

Lecture 65 Dummy variable creation in Python

Lecture 66 Correlation Analysis

Lecture 67 Correlation Analysis in Python

Section 18: Linear Regression – Model Creation

Lecture 68 The Problem Statement

Lecture 69 Basic Equations and Ordinary Least Squares (OLS) method

Lecture 70 Assessing accuracy of predicted coefficients

Lecture 71 Assessing Model Accuracy: RSE and R squared

Lecture 72 Simple Linear Regression in Python

Lecture 73 Multiple Linear Regression

Lecture 74 The F – statistic

Lecture 75 Interpreting results of Categorical variables

Lecture 76 Multiple Linear Regression in Python

Lecture 77 Test-train split

Lecture 78 Bias Variance trade-off

Lecture 79 More about test-train split

Lecture 80 Test train split in Python

Section 19: Introduction to ANN

Lecture 81 Introduction to Neural Networks and Course flow

Section 20: Single Cells – Perceptron and Sigmoid Neuron

Lecture 82 Perceptron

Lecture 83 Activation Functions

Lecture 84 Python – Creating Perceptron model

Section 21: Neural Networks – Stacking cells to create network

Lecture 85 Basic Terminologies

Lecture 86 Gradient Descent

Lecture 87 Back Propagation

Section 22: Important concepts: Common Interview questions

Lecture 88 Some Important Concepts

Section 23: Standard Model Parameters

Lecture 89 Hyperparameters

Section 24: Tensorflow and Keras

Lecture 90 Keras and Tensorflow

Lecture 91 Installing Tensorflow and Keras

Section 25: Python – Dataset for classification problem

Lecture 92 Dataset for classification

Lecture 93 Normalization and Test-Train split

Section 26: Python – Building and training the Model

Lecture 94 Different ways to create ANN using Keras

Lecture 95 Building the Neural Network using Keras

Lecture 96 Compiling and Training the Neural Network model

Lecture 97 Evaluating performance and Predicting using Keras

Section 27: Python – Solving a Regression problem using ANN

Lecture 98 Building Neural Network for Regression Problem

Lecture 99 The final milestone!

Section 28: Congratulations & About your certificate

Lecture 100 Bonus lecture

People pursuing a career in data science,Working Professionals beginning their Machine Learning journey,Statisticians needing more practical experience,Anyone curious to master Time Series Analysis using Python in short span of time

#### Course Information:

Udemy | English | 13h 18m | 5.52 GB

Created by: Start-Tech Academy

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