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Instructor-led Classroom Adult Training in Singapore - Modular Fast Track Skill-Based Trainings

Time Series Forecasting in R

Time series are all around us, from server logs, marketing and sales to high-frequency financial data. Time series analysis involves analyzing the available data to o quantitatively monitor, describe, explain, and find out the pattern or trend in the data to predict some future values. Business forecasts can be based on historical data patterns that are used to understand the past and predict future market behavior, inclusing seasonal patterns, trend estimations and growth.

In this course, learn the core techniques necessary to extract meaningful insights from time series data to make informed decision, making. The instructor will cover the basics of time series, analysis techniques, modelling, visualization and forecasting of time series.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant Applications

Click the links below to apply. Note that you need to register the course first.

For Singaporeans: SkillsFuture Credit

For Company: SSG Training Grant

Course Code: CRS-N-0048279

Course Booking

$298.00 (GST-exclusive)
$318.86(GST-inclusive)

Course Date

Course Time

* Required Fields

Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.


Course Details

Module 1: Basics of Time Series

  • Why study time series?
  • What is time series modelling?
  • Applications of time series modelling
  • Time Series vs machine learning

Module 2: Formatting Time Series

  • TS stats and queries
  • Subsetting and merging
  • Decompose and aggregation
  • Lags and differences
  • Rolling window

Module 3: Smoothing and Forecasting

  • Smoothing methods
  • Time series models
  • Machine learning models
  • Examining forecast errors

Module 4: Visualizations

  • Basic Charting
  • ACF/PACF plots
  • Lag plots

Module 5; Abnormality

  • Anomality Plots
  • Adjusting Decomposition Trend and Seasonality
  • Parameter Adjustment
  • Adjusting Alpha and Max Anoms
  • Detecting anomalities in remainders

Module 6: Sequence Analysis

  • Rendering Sequences
  • Transition States
  • Sequence complexity measures
  • Cluster Analysis
  • Forecasting pattern sequence

Course Admin

Prerequisite

This is an intermediate course. the following knowledge is assumed

Software Requirement

Please download and install the following software prior to the class

Who Should Attend

  • Data Scientists
  • Data Analysts
  • Finance Analysts
  • Marketers

Trainers

R TrainerDwight Nuwan Fonseka have a degree in Biotechnology (from NUS) ,Advanced diploma in Pharamceutical management (from MDIS) and Masters in Education (from NTU). He have 8 years experience of teaching biology at O and A levels/ IB level in international schools in Singapore and overseas.

Customer Reviews (1)

Will RecommendReview by Andrew Jim
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2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Add some more machine learning forecasting elements (Posted on 8/27/2019)

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