Time-Series Forecasting with AI for Revenue & Cost Drivers

Harness AI‑powered time‑series techniques to forecast core revenue and cost drivers, balance seasonality and trend shifts, and integrate macroeconomic signals. This course blends classical methods (ARIMA, ETS) with machine learning (Prophet, LSTM) and anomaly detection for business‑critical forecasting. Delegates will build robust models, deploy automated pipelines, and create dashboards to inform finance and operations planning in diverse industries.

TRAINING TOPICS

• Fundamentals of time‑series data and business seasonality
• Traditional forecasting: ARIMA, ETS, SARIMAX
• Machine learning models: Prophet, XGBoost, LSTM
• Feature engineering: cycles, lags, holidays, external variables
• Anomaly detection and handling in series data
• Hybrid forecasting architectures for improved accuracy
• Scenario and threshold anomaly forecasting
• Forecast pipeline automation (APIs, scheduling, alerts)
• Dashboarding forecasts and KPI visualization
• Model governance, drift monitoring, and retraining

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO

• Load, clean, and visualize time‑series revenue and cost data
• Apply ARIMA, ETS, and Prophet forecasting models
• Engineer features like seasonality, holidays, and exogenous drivers
• Build LSTM neural nets for non‑linear time‑series forecasting
• Detect and handle anomalies before model training
• Automate forecast pipelines with polling and scheduling
• Visualize forecasts and confidence intervals in BI tools
• Monitor forecast drift and retrain models over time

TRAINING IS TAILORED TO

• FP&A and financial analysts
• Data scientists and BI professionals in finance
• Demand planners and revenue operations managers
• Cost accountants and operational finance teams
• Developers deploying forecasting pipelines
• Finance decision-makers and planners

TRAINING METHODOLOGY

• Interactive lectures combining statistical and AI concepts
• Hands-on modeling in Python/Jupyter (ARIMA, Prophet, LSTM)
• Anomaly detection labs using statistical and ML methods
• Pipeline setup with Airflow or cron for recurring forecasts
• Dashboard building in Power BI / Tableau
• Peer model evaluation and governance checklist exercises

Day One:

FOUNDATIONS OF TIME‑SERIES FORECASTING

• Intro to time‑series structures: trend, seasonality, noise
• Exploratory data analysis: decomposition, autocorrelation
• Lab: build ARIMA/ETS models on revenue series
• Case study: demand seasonality analysis

Day Two:

ADVANCED FEATURE ENGINEERING & PROPHET

• Feature creation: date components, promotions, macro data
• Using Prophet for flexible trend fitting
• Lab: Prophet model for multi‑season business data
• Scenario: holiday impact analysis in revenue forecasts

Day Three:

MACHINE LEARNING & DEEP LEARNING FORECASTS

• XGBoost and Random Forest for series forecasting
• LSTM fundamentals and sequence modeling
• Lab: train an LSTM to predict cost driver series
• Compare ML vs classical methods

Day Four:

FORECAST PIPELINE AUTOMATION & ANOMALY HANDLING

• Data ingestion and scheduling (API, Airflow/cron)
• Detecting anomalies and cleaning series
• Lab: pipeline to generate and store forecasts
• Implement alert system for unusual forecasts

Day Five:

VISUALIZATION, DRIFT MONITORING & GOVERNANCE

• Building forecast dashboards with intervals and forecasts
• Monitor model performance and retraining triggers
• Lab: BI dashboard with rolling forecast performance
• Group work: forecast governance and audit playbook