Lesson 1Deployment considerations: retraining cadence, data pipelines, monitoring drift and backtesting forecastsPlan how to put revenue forecasting systems into use, including how often to retrain, automatic data flows, watching for data and idea changes, and ongoing checks to keep performance steady in shifting Gambian market conditions.
Designing robust data pipelinesScheduling retraining and updatesMonitoring data and concept driftOngoing backtesting and benchmarkingAlerting and rollback strategiesLesson 2Communicating forecasts to stakeholders: visualization of point forecasts and intervals, scenario analysis, transparency of assumptionsShare revenue forecasts well with stakeholders using clear pictures of point and range predictions, scenario checks, and open notes on assumptions, limits, and model risks relevant to Gambian banking.
Visualizing point and interval forecastsScenario and what-if analysis designExplaining drivers and key featuresDocumenting assumptions and limitsTailoring messages to stakeholdersLesson 3Machine learning time series models: random forests/gradient boosting with lagged features, XGBoost/LightGBM, and sequence models (LSTM/GRU)Train machine learning models for time series revenue prediction, including tree groups with past features and sequence models like LSTM and GRU, while dealing with unsteady patterns, seasons, and product differences in Gambian finance.
Random forests with lagged featuresGradient boosting, XGBoost, LightGBMGlobal versus local forecasting modelsSequence models with LSTM and GRUHandling nonstationarity and scalingLesson 4Formulating forecasting objectives and evaluation horizons (e.g., next 3, 6, 12 months)Set forecasting goals for core bank products by picking prediction aims, time spans, and details, and match them to business choices like budgeting, pricing, liquidity plans, and reporting needs under Gambian regulations.
Choosing revenue targets and unitsSelecting forecast horizons and frequencyAligning forecasts with business decisionsGranularity by product, segment, and regionHandling new products and short historiesLesson 5Identifying and sourcing time series data (public financial series, payment volumes, synthetic generation techniques)Learn to find, check, and get time series data for bank revenue forecasting, including internal product measures, public financial series, and made-up data that safely adds to limited or noisy past records in Gambia.
Cataloging internal product revenue seriesUsing public macro and market data sourcesCollecting payment and transaction volume dataAssessing data quality, gaps, and revisionsSynthetic data generation for stress scenariosLesson 6Training and hyperparameter tuning: grid/random search, Bayesian optimization, time-aware scoringImprove model performance with planned hyperparameter tuning methods, including grid and random search, Bayesian optimization, and time-based scoring that follows time order and focuses on key business spans in Gambian settings.
Defining search spaces and priorsGrid and random search trade-offsBayesian optimization workflowsTime-aware validation and scoringEarly stopping and resource limitsLesson 7Model ensembling and reconciliation: simple model averaging, weighted ensembles, stacking for time seriesMix several forecasting models for bank revenue using simple averages, weighted groups, and stacking, and use group reconciliation to make sure forecasts match across products, branches, and levels in Gambian banks.
Simple and weighted model averagingStacking and meta-learners for seriesDiversity and correlation among modelsHierarchical and grouped reconciliationEvaluating ensemble stability over timeLesson 8Baseline time series methods: ARIMA, ETS, naive and seasonal naïve models, decomposition (trend/seasonality)Look at basic time series models for bank revenue, including simple, seasonal simple, ARIMA, ETS, and breaking down parts, to set reference performance and understand trends and seasons before complex machine learning in Gambia.
Naive and seasonal naive benchmarksClassical decomposition of trend and seasonalityARIMA modeling for bank revenue seriesExponential smoothing and ETS variantsComparing baselines across productsLesson 9Feature engineering for revenue: lags, rolling means/std, differencing, calendar effects, holiday indicators, cohort effects, marketing/campaign flagsCreate predictive features for bank revenue, including past values, rolling stats, differences, calendar and holiday effects, group and life cycle signs, and marketing flags that catch demand shifts and breaks in Gambian markets.
Lag and lead features for revenueRolling means, volatility, and ratiosCalendar, holiday, and payday effectsCohort and lifecycle based featuresMarketing and campaign impact flagsLesson 10Data splitting and cross-validation for time series: train/validation/test splits, expanding window CV, blocked CVDesign time-based data splits and cross-checks for revenue forecasting, including rolling and blocked ways, to stop leaks, copy real use, and get solid estimates of model performance over time in Gambia.
Holdout train, validation, and test splitsRolling and expanding window validationBlocked cross-validation for seasonalityPreventing temporal leakage in featuresBacktesting over multiple forecast originsLesson 11Evaluation metrics and error analysis: MAE, RMSE, MAPE, symmetric MAPE, prediction intervals and coverageCheck revenue forecasts with measures like MAE, RMSE, MAPE, even MAPE, and range coverage, and do detailed error checks by group, span, and condition to find biases and model weak spots in Gambian finance.
Scale-dependent error metricsPercentage and relative error metricsPrediction intervals and coverageHorizon and segment level diagnosticsRegime and event-driven error analysisLesson 12Macro and external regressors: using CPI, unemployment, interest rates, mobility, Google Trends; feature selection and lag alignmentAdd big economy and outside factors into revenue models, like CPI, joblessness, interest rates, movement, and search trends, and learn ways for lag matching, scaling, and feature picking to avoid overfit in Gambia.
Selecting relevant macro indicatorsAligning lags between macro and revenueTransforming and scaling external dataFeature selection and regularizationStress and scenario overlays with macros
