Course Level: Intermediate

Course Objectives

• 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.

Course Outline

Module 1: Introduction to Time Series Analysis

• Overview of time series analysis concepts
• Key terminology and techniques
• Importance of time series forecasting in agriculture
• Case Study: Analyzing historical crop yield data for trend identification

Module 2: Time Series Components and Decomposition

• Understanding trend, seasonal, and irregular components
• Decomposition methods and techniques
• Practical application in SAS
• Case Study: Decomposing seasonal patterns in crop production data

Module 3: Data Preparation and Cleaning in SAS

• Techniques for preparing and cleaning time series data
• Handling missing values and outliers
• Data transformation and normalization
• Case Study: Cleaning and preparing soil moisture data for analysis

Module 4: Exploratory Data Analysis (EDA) for Time Series

• Techniques for exploring and visualizing time series data
• Identifying patterns and anomalies
• Using SAS for exploratory analysis
• Case Study: Visualizing and analyzing weather data trends

Module 5: Autoregressive Integrated Moving Average (ARIMA) Models

• Understanding ARIMA models and their components
• Building and validating ARIMA models in SAS
• Applications in agricultural forecasting
• Case Study: Forecasting crop yields using ARIMA models

Module 6: Seasonal Time Series Models

• Techniques for modeling seasonal time series data
• Implementing Seasonal ARIMA (SARIMA) models
• Practical examples and applications in SAS
• Case Study: Modeling and forecasting seasonal crop patterns

Module 7: Exponential Smoothing Methods

• Overview of exponential smoothing techniques
• Application of Holt-Winters method in SAS
• Comparing exponential smoothing with ARIMA models
• Case Study: Forecasting livestock demand using exponential smoothing

Module 8: Advanced Forecasting Techniques

• Introduction to advanced forecasting methods
• Implementing state-space models and dynamic regression in SAS
• Evaluating model performance and accuracy
• Case Study: Using advanced techniques for predicting grain prices

Module 9: Model Validation and Accuracy Assessment

• Techniques for validating and assessing forecasting models
• Measures of forecast accuracy and reliability
• Practical applications in SAS
• Case Study: Validating a forecasting model for fertilizer demand

Module 10: Implementing Forecasting Models in Agricultural Decision-Making

• Integrating forecasting models into decision-making processes
• Developing actionable insights from forecasts
• Case studies and practical applications
• Case Study: Implementing forecasting insights for optimizing irrigation schedules