Udemy

Platform

English

Language

Data Science

Category

Mastering Time Series Forecasting with Python

Learn Python, Time Series Model Additive, Multiplicative, AR, Moving Average, Exponential, ARIMA models

4.40 (53 reviews)

Students

11.5 hours

Content

Jun 2021

Last Update
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What you will learn

Python Programing

Basic to Advanced Time Series Methods

Time Series Visualization in Python

Auto Regressive Methods,

Moving Average, Exponential Moving Average

Linear Regression and Evaluation

Additive and Multiplicative Models

ARMA, ARIMA, SARIMA in Python

ACF and PACF

Auto ARIMA in Python

Stationary and Non Stationary

GARCH Models


Description

Welcome to Mastering Time Series Forecasting in Python

Time series analysis and forecasting is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers all types of modeling techniques for forecasting and analysis.

We start with programming in Python which is the essential skill required and then we will exploring the fundamental time series theory to help you understand the modeling that comes afterward.

Then throughout the course, we will work with a number of Python libraries, providing you with complete training. We will use the powerful time-series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, statsmodels, Sklearn, and ARCH.

With these tools we will master the most widely used models out there:

  • Additive Model

  • Multiplicative Model

  • AR (autoregressive model)

  • Simple Moving Average

  • Weighted Moving Average

  • Exponential Moving Average

  • ARMA (autoregressive-moving-average model)

  • ARIMA (autoregressive integrated moving average model)

  • Auto ARIMA



We know that time series is one of those topics that always leaves some doubts.

Until now.

This course is exactly what you need to comprehend the time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes – everything is included.




Screenshots

Mastering Time Series Forecasting with Python
Mastering Time Series Forecasting with Python
Mastering Time Series Forecasting with Python
Mastering Time Series Forecasting with Python

Content

Introduction

What is Time Series Data

Time Series Components

Download the Resources

Python Essentials

Download the Resources

Install Anaconda Python

Open Jupyter Notebook

Markdown

Print Statements

Escape and Insert keys

Variables & Assignments

Data Types

Data Type Casting

List

List Methods

Tuple

Sets

Dictionaries

in operator

concatenate & repeat operator

User Defined Functions

Control Statements (if else)

Range & Zip

For Loop

Numpy

Arrays

Shape, size, ndim

Array Creation - arange

linspace

zeros & zeros_like

ones & ones_like

Random (Uniform & Gaussian Distribution)

Poisson Random Distribution

Gamma Random Distribution

Beta Random Distribution

Generate custom array

Save Arrays in npy, npz and txt

Arithmetic Operations

Arithmetic Operations - part2

Arithmetic Operations - part3

Setting Google Colab

Install Google Colab to your mail id

Integrate Google Drive to Colab to Load Data

Time Series Visualizations

Download the Resources

Types of Charts for Time Series

Setting up Google Colab

Load the Data

Line Chart

Hue the Line Chart

Area Chart

Bar Plot

Proposition and Stacked Bar, Area Chart

Heatmaps

Linear Regression

Download the Resources

Intuition of Linear Regression

Exploratory Data Analysis

EDA - Quantitative Technique

EDA - Graphical Technique

Simple Linear Regression - Python

Simple Linear Regression - Sklearn (Python)

Simple Linear Regression - Statsmodels (Python)

Model Evaluation - R^2, ANOVA

Model Evaluation - Python

Regression for Time Series Forecasting

Regression with Time

Download the Resources

Data Preprocessing in Python

Splitting Data into Training and Testing Sets in Python

Train Regression Model with Time in Python

Forecasting with Confidence Interval and Visualizations in Python

Additive Time Series Model with Statsmodels

Additive Model

Download the Resources

Data Analysis in Python

Creating Seasonal Features

Splitting Data into Training and Testing Sets

Training Additive Model in Statsmodels

Additive Model Forecasting and Visualizations

Multiplicative Time Series Model

Multiplicative Model

Download the Resources

Step-1: Trend Model

Step-2: Calculate Seasonal Deviation

Step-3: Seasonal Corrector Factor

Fitted values and Forecasting with Multiplicative Model

Margin of Error and Confidence Interval

Visualizing Forecasted Data

Auto Regressive Methods

Auto Regressive Methods

Download the Resources

Setting Up for Model Building

Data Preprocessing

ACF & PACF

Making Data Stationary

Training AR Model

Fitted and Forecasting values with AR Model

AR Model Evaluation

Smoothing Methods (Moving Average)

Smoothing Techniques

Download the Resources

Naive Forecasting Model

Naive Forecasting Model in Python - part 1

Naive Forecasting Model in Python - part 2

Simple Moving Average

Simple Moving Average in Python

Simple Moving Average order (q) in Python

Weighted Moving Average

Weighted Moving Average in Python

Exponential Moving Average

Exponential Moving Average in Python

ARIMA , SARIMA, SARIMAX

ARIMA, SARIMA, SARIMAX

Bonus Lecture

Bonus Lecture: Next Steps


Reviews

C
Craig13 February 2021

The course should not include the word "Complete" in the title. It is an introductory course which doesn't evern get to the ARIMA model. For example, in the description of the course it states "Learn Python, Time Series Model Additive, Multiplicative, AR, Moving Average, Exponential, ARIMA, SARIMAX, GARCH models", yet for Section 12 (the last section of the course) there is nothing but a message stating "We are soon going to add the lecture on ARIMA, SARIMA, SARIMAX and GARCH models." This is totally misleading. One last point, the audio is very poor on some of the videos.


Coupons

DateDiscountStatus
7/10/2021100% OFFExpired

3333632

Udemy ID

7/15/2020

Course created date

1/21/2021

Course Indexed date
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