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2021-01-07 18:41:06
30-Day Money-Back Guarantee

This course includes:

  • 10 hours on-demand video
  • 11 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Time Series Analysis

Complete Practical Time Series Forecasting in Python

Learn Python, Time Series Model Additive, Multiplicative, AR, Moving Average, Exponential, ARIMA, SARIMAX, GARCH models
Hot & New
Rating: 4.8 out of 54.8 (4 ratings)
32 students
Created by Data Science Anywhere Team, Sudhir G
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll 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

Course content

13 sections • 104 lectures • 10h 10m total length

  • Preview02:38
  • Preview03:37
  • Download the Resources
    00:00

  • Download the Resources
    00:01
  • Install Anaconda Python
    04:06
  • Open Jupyter Notebook
    05:45
  • Markdown
    20:14
  • Preview07:12
  • Preview11:00
  • Variables & Assignments
    02:41
  • Data Types
    02:22
  • Data Type Casting
    04:55
  • List
    17:05
  • List Methods
    06:18
  • Tuple
    04:59
  • Sets
    03:45
  • Dictionaries
    09:45
  • in operator
    02:56
  • concatenate & repeat operator
    02:47
  • Preview08:01
  • Control Statements (if else)
    07:16
  • Range & Zip
    04:43
  • For Loop
    04:52

  • Arrays
    06:17
  • Shape, size, ndim
    05:08
  • Array Creation - arange
    05:08
  • linspace
    02:27
  • zeros & zeros_like
    02:36
  • ones & ones_like
    01:55
  • Random (Uniform & Gaussian Distribution)
    09:57
  • Poisson Random Distribution
    03:31
  • Gamma Random Distribution
    06:10
  • Beta Random Distribution
    06:56
  • Generate custom array
    04:18
  • Save Arrays in npy, npz and txt
    08:40
  • Arithmetic Operations
    04:40
  • Arithmetic Operations - part2
    06:50
  • Arithmetic Operations - part3
    13:39

  • Install Google Colab to your mail id
    07:14
  • Integrate Google Drive to Colab to Load Data
    07:35

  • Download the Resources
    00:01
  • Preview01:19
  • Setting up Google Colab
    06:35
  • Load the Data
    04:04
  • Preview06:32
  • Preview06:13
  • Preview03:09
  • Preview06:27
  • Preview10:28
  • Preview03:21

  • Download the Resources
    00:02
  • Preview09:23
  • Preview01:53
  • Preview11:41
  • Preview07:09
  • Preview13:46
  • Simple Linear Regression - Sklearn (Python)
    08:53
  • Simple Linear Regression - Statsmodels (Python)
    10:05
  • Model Evaluation - R^2, ANOVA
    04:50
  • Model Evaluation - Python
    09:26

  • Regression with Time
    04:02
  • Download the Resources
    00:00
  • Data Preprocessing in Python
    11:24
  • Splitting Data into Training and Testing Sets in Python
    05:53
  • Train Regression Model with Time in Python
    02:49
  • Forecasting with Confidence Interval and Visualizations in Python
    19:18

  • Additive Model
    08:51
  • Download the Resources
    00:00
  • Data Analysis in Python
    08:42
  • Creating Seasonal Features
    03:42
  • Splitting Data into Training and Testing Sets
    05:08
  • Training Additive Model in Statsmodels
    01:26
  • Additive Model Forecasting and Visualizations
    06:39

  • Multiplicative Model
    09:53
  • Download the Resources
    00:00
  • Step-1: Trend Model
    08:38
  • Step-2: Calculate Seasonal Deviation
    02:21
  • Step-3: Seasonal Corrector Factor
    02:30
  • Fitted values and Forecasting with Multiplicative Model
    07:25
  • Margin of Error and Confidence Interval
    02:02
  • Visualizing Forecasted Data
    05:41

  • Auto Regressive Methods
    04:33
  • Download the Resources
    00:00
  • Setting Up for Model Building
    07:02
  • Data Preprocessing
    12:08
  • ACF & PACF
    07:29
  • Making Data Stationary
    05:49
  • Training AR Model
    06:16
  • Fitted and Forecasting values with AR Model
    08:03
  • AR Model Evaluation
    04:24

Requirements

  • Basics knowledge in Statistics
  • Basic understand on Python
  • Should have Gmail Account and should able to open Google Drive

Description

Welcome to Complete Practical Time Series Analysis and 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 type 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)

. SARIMA (seasonal autoregressive integrated moving average model)

. SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)

· ARCH (autoregressive conditional heteroscedasticity model)

· GARCH (generalized autoregressive conditional heteroscedasticity model)


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.

Who this course is for:

  • Anyone who are interested to do time series analysis and forecasting
  • Want to do advanced real time forecasting

Instructors

Data Science Anywhere Team
Team of Engineer and Developers
Data Science Anywhere Team
  • 4.5 Instructor Rating
  • 177 Reviews
  • 6,805 Students
  • 4 Courses

Hi,

We're team of Machine Learning experts, AI developers working together to advance the state of the art in artificial intelligence. You will be hearing from us when new courses are released, answering Q&A and many more.

We are here to help you stay on the cutting edge of Data Science and Technology.


Thanks,

Data Science Anywhere Team

Sudhir G
Data Scientist
Sudhir G
  • 4.5 Instructor Rating
  • 160 Reviews
  • 6,805 Students
  • 3 Courses

Sudhir is an experienced Data Scientist with a demonstrated history of working in the information technology and services industry. Skilled in Machine Learning, Deep Learning, Statistical algorithms he mostly worked on Image Processing and Natural Language processing application. He also successfully deployed many data science-related projects in cloud platforms as a service.  Strong engineering professional with a Bachelor's degree focused on Electrical and Electronics Engineering.

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