Box-Jenkins Time Series Forecasting Methods Training Course

Course overview

Finance, planning and economics teams routinely need defensible forecasts for revenue, cash receipts, demand, exchange rates, inflation, interest rates and operating costs. Yet many forecasts rely on trend extrapolation, spreadsheet assumptions or models selected only because they produce a plausible number. This course addresses the practical challenge of turning a historical time series into a tested forecasting model: identifying recurring structure, separating signal from noise, checking whether forecasts are reliable, and explaining the model choice to decision-makers.

Participants learn the Box-Jenkins methodology for specifying, estimating, diagnosing and forecasting with ARIMA and seasonal ARIMA models. They work with autocorrelation and partial autocorrelation plots, stationarity tests, transformations, differencing, lag selection, information criteria, residual diagnostics and forecast-error measures. The course also covers intervention variables, outlier handling, seasonal effects and forecast intervals, enabling participants to build models that account for both uncertainty and operational planning requirements.

Instruction combines concise technical teaching with guided software labs using finance and economic data. Each day includes model-building exercises in R, Python, EViews and Stata, followed by review of modelling decisions and diagnostic results. Participants complete an end-to-end forecasting case and leave with a documented Box-Jenkins forecasting workbook or script, including data preparation steps, model-selection evidence, residual checks, forecast outputs and a short management briefing.

The course is suited to analysts who produce or review recurring forecasts and need a disciplined method beyond simple moving averages or regression-only approaches. It is particularly relevant where historical data are available, forecast accuracy is monitored, and management requires a clear explanation of model assumptions and risks.

Course objectives

By the end of this course, participants will be able to:

• Test time-series stationarity using visual inspection, unit-root tests and seasonal diagnostics
• Apply logarithmic transformations and regular or seasonal differencing to prepare financial and economic series
• Identify candidate ARIMA and SARIMA orders from ACF and PACF correlograms
• Estimate Box-Jenkins models in R, Python, EViews and Stata
• Select competing forecasting models using AIC, BIC, parsimony and holdout-sample evidence
• Diagnose model residuals using Ljung-Box tests, residual correlograms and normality checks
• Produce point forecasts and prediction intervals for business planning horizons
• Document a reproducible Box-Jenkins forecasting model and management-ready forecast briefing

Benefits of attending

Target competencies

Who should attend

• Financial Analysts — who prepare revenue, cash flow, cost or market forecasts for planning decisions
• Economists — who model macroeconomic indicators, prices, interest rates or exchange rates
• FP&A Managers — who need to challenge forecast assumptions and improve forecasting governance
• Treasury Analysts — who forecast liquidity, rates, currency exposures and short-term funding requirements
• Risk Analysts — who assess time-dependent financial exposures and forecast uncertainty
• Business Intelligence Analysts — who turn operational and commercial history into recurring forecast outputs

Requirements and prerequisites

Participants should be comfortable working with spreadsheets or statistical software and interpreting basic descriptive statistics, charts and regression output. Familiarity with time-indexed business data, such as monthly sales, inflation, cash receipts or exchange rates, is helpful. The course assumes basic algebra and an ability to read tables of model results; it does not assume prior ARIMA, econometrics programming or advanced calculus knowledge. Participants should bring a laptop if attending in person. Complete beginners in statistics can attend, but should expect to spend additional time reviewing variance, correlation, regression and hypothesis-testing terminology.

Training methodology

The instructor introduces each Box-Jenkins stage using worked finance and economics examples, then participants reproduce the process in guided labs. Exercises move from plotting and transforming raw series to estimating ARIMA candidates, reading diagnostic output and comparing out-of-sample accuracy. Small-group case discussions focus on practical decisions such as selecting a forecast horizon, treating exceptional observations and explaining uncertainty to management. The final day uses an end-to-end case workshop in which participants build, validate and present a forecast model with an application plan for their own workplace data.

Course outline

Day 1: Time-Series Structure and Box-Jenkins Foundations

• Forecasting uses in finance, economics and corporate planning
• The four Box-Jenkins stages: identification, estimation, diagnosis and forecasting
• Time indexing, frequency selection and missing-observation treatment
• Trend, seasonality, cycles and irregular variation
• Time-series plots and rolling summary statistics
• Autocorrelation and partial autocorrelation concepts
• Stationarity and the consequences of modelling non-stationary data

Workshop: Participants profile a monthly revenue or inflation series and produce an initial time-series diagnostic chart pack.

Day 2: Preparing Data and Identifying ARIMA Models

• Logarithmic, Box-Cox and percentage-change transformations
• Regular differencing and integration order selection
• Seasonal differencing for monthly and quarterly series
• Augmented Dickey-Fuller and KPSS stationarity tests
• Reading ACF correlograms for moving-average structure
• Reading PACF correlograms for autoregressive structure
• Candidate ARIMA and SARIMA notation: p, d, q and P, D, Q

Workshop: Participants transform and difference a seasonal cash-receipts series, then nominate justified ARIMA and SARIMA candidates.

Day 3: Estimation and Model Selection

• Maximum-likelihood estimation of ARIMA parameters
• Parameter significance, signs and invertibility constraints
• AIC, AICc and BIC model-comparison criteria
• Parsimony versus in-sample fit
• Training, validation and holdout-sample design
• Forecast accuracy measures: MAE, RMSE, MAPE and MASE
• Estimating ARIMA models in R, Python, EViews and Stata

Workshop: Participants estimate competing ARIMA models for an exchange-rate series and produce a model-selection table.

Day 4: Diagnostics, Seasonality and Forecast Risk

• Residual plots and residual autocorrelation checks
• Ljung-Box tests for remaining serial correlation
• Residual normality, heteroscedasticity and influential observations
• Outlier, level-shift and temporary-intervention effects
• Seasonal ARIMA diagnostics and seasonal residual patterns
• Dynamic forecasts, static forecasts and forecast horizon choice
• Prediction intervals and communicating forecast uncertainty

Workshop: Participants diagnose a flawed seasonal ARIMA model, correct its specification and prepare an uncertainty-focused forecast chart.

Day 5: Applied Forecasting Case and Implementation

• Forecasting with ARIMAX and intervention variables
• Calendar effects, policy changes and exceptional business events
• Forecast reconciliation with management assumptions
• Rolling-origin evaluation and model monitoring
• Model documentation, version control and reproducibility
• Presenting model evidence to non-technical stakeholders
• Designing a recurring Box-Jenkins forecasting workflow

Workshop: Participants complete an end-to-end forecasting case and produce a documented model, forecast pack and implementation plan.

Tools & standards covered

R, Python statsmodels, EViews, Stata

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions