Module 1: Introduction to Time Series Data and Components

·       Defining time series data and its characteristics (frequency, sequence)

·       Understanding the four components: Trend, Seasonality, Cyclical, and Irregular

·       Additive versus Multiplicative models for decomposition

·       The importance of setting up the time variable correctly in SPSS

·       Evaluating basic measures of forecast accuracy (MSE, RMSE, MAPE)

·       Practical session: Defining the time/date variable in SPSS and generating a basic plot to visualize the time series components

Module 2: Data Preparation and Visualization in SPSS

·       Importing, structuring, and defining time series data sets in SPSS

·       Handling missing values in time series data (Interpolation and imputation techniques)

·       Techniques for aggregating and disaggregating time series frequency

·       Data transformation techniques (log, differencing) to stabilize variance

·       Creating and interpreting sequence charts and scatter plots over time

·       Practical session: Loading a monthly sales dataset, handling missing values via interpolation, and applying a log transformation in SPSS

Module 3: Smoothing and Classical Decomposition Methods

·       The objective and uses of data smoothing techniques

·       Implementing and interpreting simple Moving Averages in SPSS

·       Applying the Classical Decomposition method (Seasonal-Trend Decomposition)

·       Interpreting the resulting trend, seasonal factors, and irregular components

·       Utilizing the seasonal indices to seasonally adjust the time series

·       Practical session: Decomposing a retail sales time series in SPSS to isolate the seasonal component and calculating the seasonal indices

Module 4: Introduction to Exponential Smoothing Models

·       Theoretical basis of Exponential Smoothing models

·       Implementing the Simple Exponential Smoothing (SES) method for data without trend or seasonality

·      Interpreting the smoothing parameter () and its effect on the forecast

·       Implementing the Holt's Linear Trend method for data with trend but no seasonality

·       Assessing the fit of the exponential smoothing models using various error metrics

·       Practical session: Running Simple Exponential Smoothing and Holt's Linear Trend methods on appropriate data sets in SPSS and comparing their RMSE values

Module 5: Advanced Exponential Smoothing: Holt-Winters Models

·       The need for the Holt-Winters (Triple Exponential Smoothing) method

·       Implementing the Additive Holt-Winters model for constant seasonality

·       Implementing the Multiplicative Holt-Winters model for increasing seasonality

·       Interpreting the three smoothing parameters (α, β, Y)

·       Automatic model selection using the SPSS Expert Modeler function

·       Practical session: Using the SPSS Expert Modeler to automatically find the best-fitting Exponential Smoothing model for a dataset with strong seasonality

Module 6: Stationarity, Autocorrelation, and Partial Autocorrelation

·       Definition of a Stationary Time Series and its importance for ARIMA modeling

·       Identifying non-stationarity (trend and level) through visual inspection

·       Calculating and interpreting the Autocorrelation Function (ACF) plot

·       Calculating and interpreting the Partial Autocorrelation Function (PACF) plot

·       Using the Differencing operator to achieve stationarity in SPSS

·       Practical session: Applying first and seasonal differencing to a non-stationary dataset in SPSS and examining the resulting ACF and PACF plots

Module 7: ARIMA Model Identification and Specification

·       Theoretical structure of the ARIMA model: AR (p), I (d), MA (q)

·       The Box-Jenkins methodology for model building (Identification, Estimation, Diagnostics)

·       Using the ACF and PACF plots to identify the non-seasonal order of  and

·       Identifying the seasonal orders (p,d,q) for SARIMA models

·       Specifying the appropriate orders in the SPSS Auto-Regressive Integrated Moving Average module

·       Practical session: Utilizing the ACF and PACF plots generated in SPSS to hypothesize the correct  order for a stationary time series

Module 8: ARIMA Model Estimation and Diagnostic Checking

·       Estimating the parameters of the specified ARIMA model in SPSS

·       Interpreting the model coefficients, their significance, and standard errors

·       Performing Residual Analysis (White Noise Test/Ljung-Box Q) to check for remaining pattern

·       Checking the normality and independence of the model residuals

·       Applying model modifications based on diagnostic failures

·       Practical session: Running the specified ARIMA model in SPSS and using the Residual ACF/PACF plots and Ljung-Box test to check for model adequacy

Module 9: Advanced Forecasting Techniques and Model Comparison

·       Generating forecasts and calculating the Confidence Intervals

·       Visualizing the forecast and its uncertainty range on a sequence chart

·       Using the Holdout Period to compare in-sample fit versus out-of-sample forecast accuracy

·       Comparing the performance of multiple competing models (e.g., ARIMA vs. Holt-Winters)

·       Techniques for evaluating and explaining forecast errors and outliers

·       Practical session: Comparing the forecasting accuracy of the best Exponential Smoothing model and the best ARIMA model on a holdout sample using the Accuracy Measures table in SPSS

Module 10: Integrating Time Series with Regression and Reporting

·       Introduction to Transfer Function models (ARIMAX) for external predictors

·       Including external predictor variables (e.g., price, promotions) in forecasting models

·       Incorporating dummy variables to model specific intervention effects or calendar events

·       Documenting and clearly presenting forecasting results and model assumptions

·       Exporting forecast data and charts from SPSS for reporting purposes

·       Practical session: Building a Transfer Function model in SPSS by including a relevant predictor variable and comparing the forecast improvement against the univariate ARIMA model

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588