Time Series Forecasting with R – London - 2-Day Training Course

Description:

1. Course description.

The “Time Series Forecasting with R” 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, prophet, forecast and zoo 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, random walks, ARMA and ARIMA approaches.

2. Programme.

The course will run for two days (Monday and Tuesday) between 9:30am and 5:00pm and will consist of alternating lecture-style presentations and practical tutorials. The example datasets used during tutorial sessions will come from social sciences, psychology, business and finance fields, however the contents may vary depending on specific interests of participants (based on the Participant’s Skills Inventory). There will be two 15-minute coffee/tea breaks and one 1-hour lunch break on each day of the course.

The programme for this course covers the following concepts and topics:

• Import, clean and pre-process time series data using standard R functionalities and its third-party libraries e.g. tidyverse family of packages (dplyr, tidyr etc.),
• Manipulate time series data structures including their indexing, subsetting and slicing,
• Convert date/time stamps into varying date/time units, convert between time series frequencies using different resampling methods and dealing with missing values,
• Carry out time series data aggregations using pivot tables, cross tabulations and data summaries,
• Decompose and visualise all components of time series data (trend, seasonality, residuals, etc.),
• Calculate moving/rolling averages and other rolling single-value statistics, lagged and shifted time series, percentage changes between data points of different time series frequencies,
• Assess stationarity of time series and perform varying methods of differencing,
• Predict future data using simple linear trend and multiple regression models for time series data including methods of measuring model accuracy and model diagnostics,
• Estimate parameters of non-linear or locally-weighted models, regression trees and random walk models,
• Perform more advanced forecasting methods using Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models,
• Measure the ARIMA model accuracy using various accuracy metrics, compare and select the models.

3. What is included?

Apart from the contents of the course, Mind Project will provide you with the following:

• printed course pack with all presentation slides, cheatsheets and other essential course information,

• digital (USB memory stick) Course Manual including all presentation slides, R course codes and a list of reference books and online resources,

• additional home exercises and all data sets available to download,

• stimulating, friendly and inclusive learning environment in a small group (typically 10-14 attendees) led by experienced and energetic tutors and course leaders,

• modern and comfortable training venue located in the heart of City of London – at the London Institute of Banking & Finance, next to the Monument underground station,

• refreshments and a light, energising lunch on each day of the course,

• Wi-Fi access,

• networking opportunity,

• Mind Project course attendance certificate.

4. Further instructions.

• In order to benefit from the course, we recommended that all attendees have the most recent version of R and R Studio software installed on their personal laptops (any operating system). As R is a free and open-source environment you can download it directly from www.r-project.org website and R Studio is available at https://www.rstudio.com/products/rstudio/#Desktop. Please contact us should you have any questions or issues with the installation process. A list of specific R packages to install will be provided at least two weeks before the first day of 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 training course (details at https://www.mindproject.io/product/applied-data-science-with-r-london-february-2019/).

• The deadline for registrations on this training course is Thursday, 21st of February 2019 at 16:00 London (UK) time. Mind Project reserves the right to end the registration process earlier if all places are booked before the deadline.

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/time-series-forecasting-with-r-london-february-2019/.

Cost:

£750 per person for the whole course (regular fee).
£600 per person for the whole course for UK registered undergraduate and postgraduate students, and representatives of registered charitable organisations (discounted fee).
For group bookings of 4 and more delegates, please contact us directly.

Website and registration:

https://www.mindproject.io/product/time-series-forecasting-with-r-london-february-2019/

Region:

Greater London

Keywords:

Correlation, Effect size , Statistical Theory and Methods of Inference, Regression Methods, Linear regression, Time Series Analysis, Forecasting, Data Mining, Neural networks, Machine learning, Quantitative Software, R, ARIMA , seasonality , trend

Related publications and presentations:

Correlation
Effect size
Statistical Theory and Methods of Inference
Regression Methods
Linear regression
Time Series Analysis
Forecasting
Data Mining
Neural networks
Machine learning
Quantitative Software
R

Back to archive...