Forecasting training course by TONEX

The purpose of Forecasting training is to cover the fundamentals and principles of linear programming, forecasting, trend analysis and simulation.Forecasting training provides a modern comprehensive survey of the principles and applications of forecasting methods in the world of commerce, public and private sectors.

The course covers all necessary theory for a rigorous statistics approach, but all basic content is presented in an intuitive style supported with applications drawn from a wide variety of real sources and case studies.

Coverage ranges from a review of basic statistics, to thorough discussions of basic methods such as smoothing, trend, and regression, up to advanced techniques such as ARIMA, MARIMA, Neural Networks, Econometrics, and Intervention Analysis. The course builds a solid foundationto create a forecasting framework for your need.

Who Should Attend

The course is appropriate for anyone in Business, Economics, IT, or Engineering, as well as ‘time series' or regression/forecasting in statistics where an applications-oriented approach is desired.

  • Outline
    • INTRODUCTION TO forecasting and TRENDS analysis
      • Forecasting Models and Trend Analysis Applied
      • Subjective Models
      • Delphi Methods
      • Causal Models
      • Regression Models
      • Time Series Models
      • Moving Averages
      • Exponential Smoothing
      • Elements of a Good Forecast
      • Steps in the Forecasting Process
      • Techniques for Trend
      • Measures of Forecast Error
      • Forecast error
      • Forecasting Performance
      • How good is the forecast?
      • Mean Forecast Error (MFE or Bias)
      • Mean Absolute Deviation (MAD)
      • Mean squared error (MSE)
      • Mean absolute percentage error (MAPE)
      • Absolute deviation
      • Bias
      • Tracking signal
      • Adjusted Exponential Smoothing Forecasting Method
      • Defining the Method
      MODELING AND ANAYSIS
      • The basic steps in a forecasting task
      • Associative Forecasting
      • Predictor variables - used to predict values of variable interest
      • Regression - technique for fitting a line to a set of points
      • Least squares line - minimizes sum of squared deviations around the line
      • Seasonality
      • Regression
      • Multiple Regression
      • Scatter Diagram
      • Confidence Intervals
      • Error Sum of Squares (EMS)
      • The Components of a Time Series
      • Elements of a Good Forecast
      • Cycles, Seasonal Decomposition and Exponential Smoothing Models
      • Variables
      • Constraints
      • Coefficients
      • Steps in the Forecasting Process
      Techniques for Trend analysis
      • Common Nonlinear Trends
      • Applications of forecasting
      • Simple moving average
      • Cumulative moving average
      • Weighted moving average
      • Exponential moving average
      • Why is it exponential?
      • Double exponential smoothing
      • Modified moving average
      • Autoregressive moving average (ARMA)
      • Autoregressive integrated moving average (ARIMA)
      • Extrapolation
      • Linear prediction
      • Trend estimation
      • Growth curve
      • Trend-Corrected Exponential Smoothing (Holt’s Model)
      • Trend- and Seasonality-Corrected Exponential Smoothing
      • Time-Critical Decision Modeling and Analysis
      • Neural Network: For time series forecasting, the prediction model
      • Least Squares Method
      • Autoregressive moving average (ARMA)
      • Autoregressive integrated moving average (ARIMA)
      TIME-CRITICAL DECISION MODELING AND ANALYSIS
      • Causal Modeling and Forecasting
      • Smoothing Techniques
      • Box-Jenkins Methodology
      • Filtering Techniques
      • Modeling Capacity Planning with Time Series
      • Cost/Benefit Analysis
      • Modeling for Forecasting
      • Stationary Time Series
      • Statistics for Correlated Data

      CAUSAL MODELING AND FORECASTING

      • Modeling the Causal Time Series
      • How to Do Forecasting by Regression Analysis
      • Predictions by Regression
      • Planning, Development, and Maintenance of a Linear Model
      • Trend Analysis
      • Modeling Seasonality and Trend
      • Trend Removal and Cyclical Analysis
      • Decomposition Analysis

      The Components of a Time Series

      • Using Smoothing Methods in Forecasting
      • Measures of Forecast Accuracy
      • Using Trend Projection in Forecasting
      • Using Regression Analysis in Forecasting
      • The Components of a Time Series
      • Using Smoothing Methods in Forecasting
      • Measures of Forecast Accuracy
      • Using Trend Projection in Forecasting
      • Using Regression Analysis in Forecasting

      SMOOTHING TECHNIQUES

      • Moving Averages and Weighted Moving Averages
      • Moving Averages with Trends
      • Exponential Smoothing Techniques
      • Exponenentially Weighted Moving Average
      • Holt's Linear Exponential Smoothing Technique
      • The Holt-Winters' Forecasting Technique
      • Forecasting by the Z-Chart

      SPECIAL MODELING TECHNIQUES

      • Neural Network
      • Modeling and Simulation
      • Probabilistic Models
      • Delphi Analysis
      • System Dynamics Modeling
      • Transfer Functions Methodology
      • Testing for and Estimation of Multiple Structural Changes
      • Combination of Forecasts
      • Measuring for Accuracy
      PROBABILITY THEORY AND STATISTICS
      • Theory of Queuing
      • Familiar queuing problems
      • Characterizing a queue
      • Basic metrics
      • Throughput, busy time, utilization, response time, load, service time
      • Response time relationships for some simple queues
      • Distribution functions
      • Combination of random variables
      • Time Interval Distributions
      • Exponential distribution
      • Steep distributions
      • Flat distributions
      • Cox distributions
      • Other time distributions
      • Observations of life{time distribution

      Data Collection Approaches

      • Trend analysis
      • Interpretation
      • Key Concepts and Methods
      • Collecting information
      • Why Do Trend Analysis?
      • Preparing to Analyze Trend Data
      • Analysis of Trend Data
      • Presentation of Trend Data
      LABS AND HANDS-ON EXERCISES
      • Forecasting and Trend Analysis Labs
      • Trend
      • Multiple Regression
      • Seasonal Analysis
      • Exponential Smoothing
      • Lags
      • Stationarity
      • Time Series
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