Time Series And Forecasting

Decomposition and Forecasting Methods

R, Machine Learning
Pre-Requisite:
- Regression Models
Difficulty: Intermediate+
Associated Specializations:
- Machine Learning
Duration: 4 Days

Ad-Hoc Course Registration:

ON-SITE CLASS

Date: 1 – 4 February 2021
Time: 18.30 – 21.30
Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
Investment: Rp. 5.200.000

ONLINE CLASS

Date: 1 – 4 February 2021
Time: 18.30 – 21.30
Investment: Rp. 2.600.000

Course details :

Decomposition of time series allows us to learn about the underlying seasonality, trend and random fluctuations in a systematic fashion. In this workshop, we learn the methods to account for seasonality and trend, work with autocorrelation models and create industry-scale forecasts using modern tools and frameworks.

We strongly recommend that you complete the pre-requisite workshops prior to taking this course. Some concepts presented throughout the lecture may be less-than-ideal for practitioners who have not completed the pre-requisite courses.

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Schedule

Working with Time Series
Day 1
Time Series in Action
Day 1
Classical Decomposition
Day 2
Techniques to Work with Time Series
Day 3
Learn-by-Building
Day 4

Course Producer

Samuel Chan

An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.

Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.

Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.

4-Day Workshop Modules

Syllabus: Time Series & Forecasting

Module 1: Time Series I

Working with Time Series

Application of Time Series
Definition of a ts Object
Functions to Work with Time Series

Time Series in Action

Indonesia’s Gas Emissions, 1970-2012
Frequency, Start and End
Time Series Plots

Classical Decomposition

Trend, Seasonality and Residuals
Understanding Lags
Additive vs Multiplicative

Classical Decomposition in Action

Monthly Airline Passenger, 1949-1960
The decompose Function
Understanding Smoothing

Techniques to Work with Time Series

Adjusting for Seasonality
Detrending
Decomposing Non-Seasonal Time Series

Module 2: Forecasting

Forecasting I

Simple Moving Average
Simple Moving Average from First Principles
Log-Transformation

Forecasting II

Forecasting Using One-sided SMA
Forecasting Using Exponential Smoothing
Holt’s Exponential Smoothing

Forecasting III

The beta and gamma Coefficients
Mathematical Details
Holt-Winters Exponential Smoothing

Advanced Time Series

ACF and PACF
ARMA and ARIMA Models
Stationarity and Differencing

Advanced Time Series II

Augmented Dickey-Fuller (ADF) Test
Seasonal ARIMA
Tips to Work With xts
Facebook’s Prophet
Quantmod for Quantitative Traders

Academy Modules

Graded Quiz

Learning-by-Building Module (3 Points)

Forecasting the Crime rate in Chicago

Download the dataset from Chicago Crime Portal, and use a sample of these data to build a forecasting project where you inspect the seasonality and trend of crime in Chicago. Submit your project in the form of an RMD format, and address the following questions:
- Is crime generally rising in Chicago in the past decade (last 10 years)?
- Is there a seasonal component to the crime rate?
- Which time series method seems to capture the variation in your time series better? Explain your choice of algorithm and its key assumptions

The student should be awarded the full (3) points if they address at least 2 of the above questions.

Ad-Hoc Course Registration:

ON-SITE CLASS

Date: 1 – 4 February 2021
Time: 18.30 – 21.30
Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
Investment: Rp. 5.200.000

ONLINE CLASS

Date: 1 – 4 February 2021
Time: 18.30 – 21.30
Investment: Rp. 2.600.000

Workshop Receivables:

Workshop Lecturer’s Notes
Including 2x Course Books (PDF), HTML files, course transcripts (if any).
Highly-accelerated Learning
Learn under the assistance of mentorship of our lead instructor and a band of qualified teaching assistants throughout the 4-day course.
Certification of Completion
Show current and prospective employers that you’ve completed the course with a signed certificate of completion.
Quality Learning Environment
We pay meticulous attention to the logistical details of our workshops: quality audio and visual setups, comfortable sitting arrangements, small group size. Dinners are included for evening workshops.
Supplement Materials
Receive supplement datasets to practice on, reference notes, working files (R Notebook or Jupyter Notebook), and other materials that will help you master the topics.

This workshop is recommended for:

The Time Series and Forecasting workshop is an intermediate-level programming workshop best suited to R programmers that are taking their first steps into data science and machine learning.

Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.

Past Workshops in this Series:

Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.

Part of the Machine Learning Specialization

This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.

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WhatsApp: 0816-692-471
Email: community@algorit.ma

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Time Series And Forecasting

Ad-Hoc Course Registration:

Course details :

Schedule

Course Producer

Samuel Chan

4-Day Workshop Modules

Module 1: Time Series I

Working with Time Series

Time Series in Action

Classical Decomposition

Classical Decomposition in Action

Techniques to Work with Time Series

Module 2: Forecasting

Forecasting I

Forecasting II

Forecasting III

Advanced Time Series

Advanced Time Series II

Academy Modules

Graded Quiz

Learning-by-Building Module (3 Points)

Ad-Hoc Course Registration:

Workshop Receivables:

Workshop Lecturer’s Notes

Highly-accelerated Learning

Certification of Completion

Quality Learning Environment

Supplement Materials

This workshop is recommended for:

Past Workshops in this Series:

Hands-on Coding

Working through real-life examples

Demo and Presentation

Learn-by-building philosophy

Part of the Machine Learning Specialization

Get In Touch

Data Science School

Company