Time Series Forecasting & Predictive Analytics

In today’s data-driven enterprises, the ability to forecast trends, demand, and performance accurately is a strategic advantage. This 5-day corporate training program focuses on Time Series Forecasting and Predictive Analytics — critical skills for finance, operations, supply chain, and marketing teams. Participants will learn to analyze temporal data, build forecasting models, and apply predictive analytics to business decision-making. The course covers classical statistical models such as ARIMA, exponential smoothing, and modern machine learning techniques including LSTM networks and Prophet. Through hands-on exercises and case studies, participants will gain expertise in building and evaluating forecasting models that drive actionable business insights.

Objectives of the Training

Understand the fundamentals of time series data and predictive analytics.
Learn to preprocess, visualize, and analyze temporal datasets.
Build and evaluate statistical forecasting models like ARIMA and Exponential Smoothing.
Explore machine learning and deep learning models for forecasting (Random Forest, LSTM, Prophet).
Apply predictive analytics to real-world business problems in finance, operations, and marketing.
Interpret model outputs to guide data-driven business strategy.

Prerequisites

Basic understanding of statistics, data analysis, and regression models.
Familiarity with Python and its data libraries (pandas, NumPy, matplotlib, scikit-learn).
Some exposure to business forecasting or analytics concepts is beneficial.

What You Will Learn

Time series concepts, components, and decomposition.
Building and validating classical and modern forecasting models.
Implementing predictive models using Python libraries like statsmodels, Prophet, and TensorFlow.
Evaluating forecasting accuracy using MAPE, RMSE, and cross-validation.
Business use cases of predictive analytics in multiple domains.
Creating forecasting dashboards and reporting insights for decision-makers.

Target Audience

This program is ideal for Data Scientists, Business Analysts, Financial Analysts, Operations Managers, and Decision Scientists involved in forecasting, planning, or predictive modeling. It is also suitable for professionals working in sectors like retail, banking, energy, and logistics where forecasting is a key function.

Detailed 5-Day Curriculum

Day 1 – Foundations of Time Series Analysis (6 Hours)

Session 1: Introduction to Time Series and Predictive Analytics Concepts.
Session 2: Components of Time Series – Trend, Seasonality, Noise, and Cyclicity.
Session 3: Data Exploration, Visualization, and Stationarity Testing (ADF Test, KPSS).
Hands-on: Exploring and Visualizing Real-World Time Series Data (Stock Prices, Sales, or Demand).

Day 2 – Statistical Forecasting Models (6 Hours)

Session 1: Moving Average, Exponential Smoothing, and Holt-Winters Models.
Session 2: AR, MA, ARMA, and ARIMA Models – Building and Interpreting Forecasts.
Session 3: Seasonal ARIMA (SARIMA) and Model Diagnostics (ACF, PACF).
Workshop: Building a Sales Forecasting Model using ARIMA and Holt-Winters.

Day 3 – Machine Learning for Forecasting (6 Hours)

Session 1: Framing Forecasting Problems as Supervised Learning Tasks.
Session 2: Regression and Ensemble Methods for Forecasting (Random Forest, XGBoost).
Session 3: Feature Engineering for Temporal Data – Lag, Rolling Windows, and Seasonal Features.
Hands-on: Developing ML-Based Forecasting Pipelines using scikit-learn.

Day 4 – Deep Learning and Advanced Predictive Models (6 Hours)

Session 1: Introduction to Recurrent Neural Networks (RNN) and LSTM Models for Time Series.
Session 2: Using Facebook Prophet for Business Forecasting and Trend Analysis.
Session 3: Combining Statistical and ML Models (Hybrid Forecasting Approaches).
Workshop: Building a Demand Forecasting Model using LSTM and Prophet.

Day 5 – Business Applications and Capstone Project (6 Hours)

Session 1: Business Case Studies – Forecasting in Finance, Supply Chain, and Retail.
Session 2: Capstone Project – Building and Evaluating an End-to-End Forecasting Pipeline.
Session 3: Presenting Forecasting Results – Visualization and Executive Reporting.
Panel Discussion: Future of Predictive Analytics – AI, Automation, and Real-Time Forecasting.

Capstone Project

Participants will develop a complete time series forecasting pipeline using a real dataset — from data preprocessing and feature engineering to model training, validation, and visualization. The project may involve forecasting product demand, energy consumption, or financial metrics, and participants will present their findings with visual dashboards and business recommendations.

Future Trends in Time Series Forecasting and Predictive Analytics

Time series forecasting is evolving with the integration of AI, deep learning, and cloud-based analytics platforms. Enterprises are adopting AutoML and real-time forecasting pipelines for dynamic decision-making. With advancements in probabilistic forecasting, federated learning, and MLOps, predictive analytics is becoming increasingly explainable, scalable, and integrated with business intelligence systems. Professionals equipped with these skills will play a vital role in driving data-informed enterprise transformation.