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Time Series Analysis & Forecasting with R - 6-week tutor-led online course

Time Series Analysis & Forecasting with R - 6-week tutor-led online course

Nov 03, 2020 - Dec 08, 2020

Time Series Analysis & Forecasting with R - 6-week tutor-led online course

Time Tue Nov 03 2020 at 02:30 pm to Tue Dec 08 2020 at 05:00 pm

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Online

GBP 270
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Time Series Analysis & Forecasting with R - 6-week tutor-led online course | Online Event | AllEvents.in Time Series Analysis & Forecasting with R - 6-week tutor-led online course
Learn and apply modern time series analysis and forecasting models to predict events and data in the future.

About this Event

1. Course description.

The “Time Series Analysis and Forecasting with R” online training course will provide you with essential knowledge to allow wrangling, processing, analysis and forecasting of time series data using specialised libraries such as ts, xts, zoo, tsibble, prophet, fable and forecast for R programming language. Whether you wish to analyse financial data, predict sales or marketing revenue, or understand temporal patterns in your social, medical or economic data, this course will provide you with theoretical and practical understanding on how to clean, visualise and model time series data in your workflows using R programming language.

During the course, you will first learn to manipulate the imported data, extract necessary date/time stamps and transform the processed data into supported time series R objects. You will then proceed to perform essential time series exploratory and decomposition operations, calculate selected moving/rolling single-value statistics, convert between differing time frequencies, visualise and prepare data for predictions. The forecasting part will include sessions on estimating linear, non-linear and locally-weighted trends, multiple regression models, ARMA and ARIMA approaches, dynamic models and a selection of machine learning and AI methods applicable to time series data e.g. Support Vector Machines and Long-Short Term Memory deep learning methods.


2. Course schedule.

This instructor-led course duration is planned over 6 teaching weeks (to qualify for the Course Attendance Certificate) plus an additional 1 calendar month for the completion of the data science project (to obtain the graded Course Completion Certificate).

In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills working through set tasks and homework exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment for the Course Attendance Certificate is 40-50 hours over 6 teaching weeks, and for the Course Completion Certificate it will equate to 70-80 hours (over 2.5-month period) including the project report writing time.

Start date: Tuesday, 3rd of November 2020 @14:30 London (UK) time

Schedule of sessions: Every Tuesday at 14:30 London (UK) time for 6 weeks

Deadline for registrations: Friday, 30th of October 2020 @ 17:00 London (UK) time

Week 1: Working with time series data in R - Part 1

  • Challenges with time series data with R,
  • Importing time series data,
  • Converting between different time series objects,
  • Extracting specific components of data and time.

Week 2: Working with time series data in R - Part 2

  • Plotting time series data with ggplot2,
  • Downsampling and upsampling time series,
  • Handling time series missing values,
  • Exploratory analysis of time series data.

Week 3: Time series analysis with R

  • Building on exploratory analysis of time series: moving averages, lagged values and rolling statistics,
  • Time series decomposition methods,
  • Autocorrelation, stationarity and differencing,
  • Transformations and adjustments.

Week 4: Introduction to time series forecasting methods with R

  • Simple forecasting approaches: naive model, average model, linear trend model,
  • Using decomposition for forecasting,
  • Evaluating forecasting accuracy,
  • Introduction to univariate time series methods: simple exponential smoothing, Holt’s linear trend and Holt-Winter’s seasonal methods.

Week 5: Univariate time series forecasting methods

  • Exponential smoothing state space models,
  • Autoregressive (AR) and moving average (MA) models, non-seasonal and seasonal ARIMAs,
  • Facebook’s prophet library for univariate time series forecasting,
  • Introduction to deep learning for univariate time series with Long Short-Term Memory (LSTM).

Week 6: Multivariate time series forecasting methods (and AI)

  • Multiple linear regression with time series data,
  • Polynomial regressions with time series data,
  • Combining ARIMAs with multiple linear regressions: dynamic regression models,
  • Support vector machines (SVMs) and kernel smoothing methods with multivariate time series,
  • Long Short-Term Memory for multivariate time series forecasting.

Additionally, in order to receive the full Course Completion Certificate, you will have to submit a short data analysis report (up to 2,000 words) along with Python data processing and analysis scripts within one calendar month from the last day of Week 6. The project will be assessed and graded. You will also receive a formal written feedback about your project.


3. Course pre-requisites and further information.

  • We recommend that you have the most recent version of R and R Studio software installed on your PC (any operating system). As R is a free and open-source environment you can download it directly from https://cloud.r-project.org/ website and RStudio Desktop is available at https://rstudio.com/products/rstudio/download/. A list of specific R packages to install will be shared with registered learners before the course.
  • We recommend that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. A good knowledge of statistics would be beneficial. We suggest that the course is preceded with our "Applied Data Science with R" open-to-public tutor-led online training course.
  • Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) during the tutor-led video sessions.
  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) in order to attend the video-streamed tutorials. You may also use your mobile phone (Android or iOS) to connect to our tutor-led video sessions.
  • The primary spoken and written language of the course is English.

Should you have any questions please contact Mind Project Ltd at aW5mbyB8IG1pbmRwcm9qZWN0ICEgY28gISB1aw== or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/time-series-analysis-and-forecasting-with-r-tutor-led-online-course-nov20/.





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Ticket Information Ticket Price
Regular fee GBP 420
Discounted fee GBP 270
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About The Host: Big Data predictive analytics and visualisations. Statistical computing and Big Data training.
Website Link: https://www.mindproject.io
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Date & Time

Tue Nov 03 2020 at 02:30 pm to Tue Dec 08 2020 at 05:00 pm
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