Time Series Analysis for Agricultural Forecasting using SAS Course


The Time Series Analysis for Agricultural Forecasting using SAS course provides participants with a comprehensive understanding of how to analyze and forecast agricultural data using SAS software. Through this course, participants will learn essential concepts such as trend analysis, seasonality detection, and decomposition techniques for agricultural time series. They will also explore advanced topics like ARIMA and exponential smoothing models, as well as dynamic regression and hierarchical forecasting methods. With hands-on exercises and practical examples, participants will gain the skills necessary to evaluate model performance and select the most accurate forecasting models for agricultural data.

Course Duration

10 Days

Who Should Attend?

  • Agricultural analysts and researchers
  • Data scientists and analysts
  • Statisticians
  • Professionals working in the agricultural industry

  • Understand the importance of time series analysis in the context of agricultural forecasting.
  • Gain proficiency in using SAS software for data preprocessing, analysis, and forecasting in the agricultural domain.
  • Learn techniques for identifying and modeling trends in agricultural time series data.
  • Develop the skills to detect and analyze seasonal patterns in agricultural data.
  • Master the use of ARIMA models for forecasting agricultural variables.
  • Explore exponential smoothing models and their application to agricultural forecasting.
  • Learn the seasonal decomposition of time series (STL) technique for agricultural data.
  • Acquire knowledge of advanced forecasting methods such as dynamic regression and hierarchical forecasting in the agricultural context.
  • Gain hands-on experience through practical exercises and real-world examples.
  • Develop the ability to evaluate model performance and select the most accurate forecasting models for agricultural data.

Module 1: Introduction to Agricultural Forecasting and Time Series Analysis

  • Overview of agricultural forecasting and its importance
  • Introduction to time series analysis in the context of agriculture
  • Understanding agricultural data patterns and trends

Module 2: Preparing Agricultural Time Series Data

  • Data collection and preprocessing for agricultural time series analysis
  • Handling missing values and outliers in agricultural data
  • Exploratory data analysis techniques for agricultural time series

Module 3: Trend Analysis and Seasonality in Agricultural Data

  • Identifying and modeling trends in agricultural time series
  • Detecting and analyzing seasonal patterns in agricultural data
  • Decomposition techniques for trend and seasonality extraction

Module 4: ARIMA Models for Agricultural Forecasting

  • Introduction to ARIMA (AutoRegressive Integrated Moving Average) models
  • Model identification, parameter estimation, and model selection
  • Forecasting agricultural variables using ARIMA models

Module 5: Exponential Smoothing Models for Agricultural Forecasting

  • Understanding exponential smoothing models for agricultural data
  • Single, double, and triple exponential smoothing techniques
  • Forecasting agricultural variables using exponential smoothing models

Module 6: Seasonal Decomposition of Time Series (STL) for Agricultural Data

  • Introduction to STL decomposition for agricultural time series
  • Seasonal adjustment and trend estimation using STL
  • Forecasting agricultural variables using STL decomposition

Module 7: Advanced Techniques for Agricultural Time Series Analysis

  • Dynamic regression models for incorporating external factors in forecasting
  • State space models for capturing complex dynamics in agricultural data
  • Hierarchical forecasting for multi-level agricultural data structures

Module 8: Model Evaluation and Forecast Accuracy Metrics for Agricultural Data

  • Assessing model performance and accuracy in agricultural forecasting
  • Evaluation metrics such as MAE, RMSE, and forecast error measures
  • Comparing and selecting the best models for agricultural time series

Enroll for this Course

We are proud to offer this course in a variety of training formats to suit your needs.


Enroll for a Face-to-Face (In-Person) Class

We use the highest quality learning facilities to make sure your experience is as comfortable as possible.

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Enroll for a Virtual (Zoom) Class

Join a scheduled class with a live instructor and other delegates.

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Enroll for an Online Self-Paced Class

Keep track of your own progression throughout your course and ensure continuous improvement.

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Benefits of Taking a Course at IRES


Our courses are carefully curated to keep you abreast of latest industry trends, technological advancements, and best practices. We employ a variety of teaching methodologies, including hands-on workshops, case studies, and interactive sessions, all aimed at fostering an engaging and effective learning environment. Our expert instructors bring a wealth of knowledge and real-world experience, providing our clients with insights that can be immediately applied in their professional lives.


Our courses serve as a vibrant platform for professionals to connect and engage with a diverse community of peers, industry leaders, and experts. By participating in our programs, you gain access to an invaluable network that spans across various sectors and geographical boundaries. This networking aspect is not just about forming professional relationships; it's about creating a supportive ecosystem where ideas, opportunities, and collaborations can flourish.


Our courses are designed to challenge and inspire professionals to step out of their comfort zones and explore new horizons. Through a combination of theoretical knowledge and practical application, our programs help professionals refine their existing skills and acquire new ones, making them more versatile and competitive.

FAQs & Course Administration Details:

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 Phone: +254 715 077 817 or Email: [email protected].
The instructor led trainings are delivered using a blended learning approach and comprise of presentations, guided sessions of practical exercise, web-based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields. All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.
Upon successful completion of this training, participants will be issued with an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).
Payment should be transferred to IRES account through bank on or before start of the course. Send proof of payment to [email protected].
Accommodation and airport pickup are arranged upon request. For reservations contact the Training Officer. Email: [email protected] Phone: +254 715 077 817.