Course Overview
Time Series Analysis has wide applicability in economic and financial fields but also to geophysics, oceanography, atmospheric science, astronomy, engineering, among many other fields of practice. This course will illustrate time series analysis using many applications from these fields. This course offers an introduction to commonly used time series models along with detailed implementation of the models within real data examples using the R statistical software.
Duration
5 days
Target Audience
- Data Analysts
- Statisticians
- Researchers in Economics and Finance
- Business Analysts
- Forecasting Specialists
- Economists
- Quantitative Analysts
- Financial Analysts
- Data Scientists
Organizational Impact
- Enhanced ability to forecast future trends and make informed decisions.
- Improved accuracy in resource planning and inventory management.
- Increased efficiency in analyzing and interpreting time-based data.
- Better strategic planning through detailed time series insights.
- Strengthened data-driven decision-making capabilities across departments.
Personal Impact
- Acquire practical skills in time series analysis using R.
- Gain proficiency in forecasting techniques and model evaluation.
- Enhance your analytical abilities and expertise in handling time-based data.
- Increase your value as a data professional with specialized time series knowledge.
- Develop confidence in applying time series methods to real-world problems.
Course Level:
Course Objectives
- Understand the fundamentals of time series analysis and its applications.
- Learn to manipulate and visualize time series data using R.
- Apply various time series forecasting methods, including ARIMA and exponential smoothing.
- Evaluate and validate time series models for accuracy and reliability.
- Develop practical skills in interpreting and presenting time series results for decision-making.
Course Outline
Module 1: Introduction to Time Series Analysis
- Overview of time series data and its characteristics
- Understanding components of time series (trend, seasonality, noise)
- Time series data visualization and exploration in R
- Introduction to time series objects in R (e.g., ts, xts, zoo)
- Case Study: Visualize and explore a historical dataset (e.g., monthly sales data) to identify trends and seasonal patterns.
Module 2: Data Preparation and Preprocessing
- Importing and cleaning time series data in R
- Handling missing values and outliers in time series
- Resampling and aggregating time series data
- Transformations and normalization of time series data
- Case Study: Clean and preprocess a dataset with missing values and outliers, and prepare it for analysis.
Module 3: Statistical Methods for Time Series Analysis
- Decomposition of time series data into trend, seasonal, and residual components
- Applying moving averages and exponential smoothing
- Introduction to ARIMA (AutoRegressive Integrated Moving Average) models
- Model diagnostics and validation
- Case Study: Decompose a time series dataset, apply ARIMA modeling, and evaluate model performance.
Module 4: Advanced Time Series Techniques
- Seasonal decomposition using STL (Seasonal and Trend decomposition using Loess)
- Forecasting with exponential smoothing methods (e.g., Holt-Winters)
- Implementing advanced time series models (e.g., GARCH for volatility modeling)
- Model selection and comparison techniques
- Case Study: Apply advanced forecasting techniques to predict future values in a time series dataset and compare model performance.
Module 5: Visualization and Interpretation of Time Series Results
- Creating advanced time series plots (e.g., autocorrelation plots, seasonal plots)
- Visualizing forecast results and confidence intervals
- Interpreting time series analysis results for decision-making
- Communicating findings with stakeholders using R
- Case Study: Generate a comprehensive report with visualizations and forecasts for a time series dataset and present actionable insights.
Related Courses
Course Administration Details:
Methodology
These instructor-led training sessions are delivered using a blended learning approach and include presentations, guided practical exercises, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience as professionals and trainers in these fields. All facilitation and course materials are offered in English. Participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).
Training Venue
The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch. All participants will additionally cater to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation and Airport Transfer
Accommodation and Airport Transfer are arranged upon request. For reservations contact the Training Officer.
- Email: [email protected]
- Phone: +254715 077 817
Tailor-Made
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location. For further inquiries, please contact us on:
- Email: [email protected]
- Phone: +254715 077 817
Payment
Payment should be transferred to the IRES account through a bank on or before the start of the course. Send proof of payment to [email protected]
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