Time Series Forecasting with Python

Program Description

This two-day technical program is designed for technical executives (CTOs, Heads of Data, and Lead Analysts) to master the architectural and mathematical foundations of temporal data analytics. In the Malaysian corporate sector - from managing festive season retail spikes (Hari Raya/CNY) to predicting energy loads and financial market volatility - time is the most critical variable.
This program bridges the gap between Traditional Statistical Models (ARIMA/ETS), Machine Learning (XGBoost), and Deep Learning (LSTMs/Transformers).
Participants will learn to architect production-ready forecasting pipelines, leverage Generative AI for rapid feature engineering, and implement PDPA-compliant predictive systems for high-stakes decision-making.

While this outline serves as a foundational framework with use cases from multiple industries and functions, the final program is fully customized to your industry and internal workflows.

Participants work on real-world problems, not generic examples. We engage in a pre-workshop alignment to inject your specific organizational datasets, pain points, and proprietary use cases directly into the curriculum.

Learning Objectives

Master Temporal Data Architectures: Understand the technical nuances of stationarity, seasonality, and autocorrelation in corporate datasets.
Architect Hybrid Forecasting Pipelines: Design systems that combine the statistical rigor of Statistical Models with the non-linear power of Machine Learning.
Leverage GenAI for Feature Engineering: Utilize LLMs to automate the creation of temporal features (lags, rolling windows) and synthetic data generation.

Program Details

Content

Day 1: Foundations, Stats & Machine Learning

Module 1: The Time Series Stack for Executives

Deconstructing the components of time series: Trend, Seasonality, and Residuals. Understanding the “Stationarity” requirement and why time series data breaks traditional ML assumptions.
Scenario (Retail/E-commerce): Analyzing five years of Malaysian retail data to decompose the impact of the “double-digit” sales (11.11, 12.12) versus organic growth trends.
Hands-on: Python-based EDA – Using Statsmodels to perform AD-Fuller tests and seasonal decomposition on a corporate sales dataset.
Expected Impact: Technical clarity on why standard regression fails for temporal data and how to prepare data correctly.

Module 2: Statistical Rigor – ARIMA, SARIMA & Exponential Smoothing

Mastering the “Classical” baseline. Deep dive into Autoregressive and Moving Average components. When to use statistical models over deep learning for better interpretability.
Demo (Banking/Finance): Using SARIMA to forecast daily cash liquidity requirements for a Malaysian branch network, accounting for month-end salary spikes.
Hands-on: Building an automated ARIMA selector (Auto-ARIMA) in Python to find the optimal (p,d,q) parameters for a finance dataset.
Expected Impact: Capability to lead teams in building low-compute, high-accuracy baseline models for stable environments.

Module 3: Machine Learning for Time Series (XGBoost & Prophet)

Converting time series into a supervised learning problem using “Lag Features.” Leveraging Facebook Prophet for “Business-Friendly” forecasting with holiday effects.
Scenario (Logistics/Supply Chain): Predicting warehouse throughput during the monsoon season by integrating weather data as an exogenous variable.
Hands-on: Engineering temporal features with GenAI – Using an LLM to generate Python code for complex rolling-window statistics and holiday encoding.
Expected Impact: Ability to integrate non-linear external factors (weather, holidays, oil prices) into a unified forecast.

Module 4: Technical PDPA & Temporal Data Security

Implementing “Privacy-Preserving Forecasting.” Hashing timestamps and using k-anonymity for sequential data to prevent re-identification of Malaysian consumers.
Scenario (HR/Operations): Forecasting workforce turnover without exposing individual employee exit dates or sensitive PII (Personally Identifiable Information).
Hands-on: Implementing data masking on a time-indexed dataset to ensure 100% compliance with PDPA 2.0 while maintaining analytical utility.
Expected Impact: Structural security embedded into the predictive pipeline, protecting corporate and individual data.

Day 2: Deep Learning, GenAI & Production MLOps

Module 5: Deep Learning for Sequences – LSTMs & GRUs

Introduction to Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. Understanding “Memory” in deep learning architectures.
Scenario (Manufacturing): Predicting “Remaining Useful Life” (RUL) of factory machinery in a Penang-based semiconductor plant using high-frequency sensor telemetry.
Hands-on: Building a many-to-one LSTM model in PyTorch or TensorFlow to predict power grid load fluctuations.
Expected Impact: Technical mastery over high-dimensional datasets where traditional stats cannot capture long-term dependencies.

Module 6: The Frontier – Transformers & Informers in Forecasting

The shift from RNNs to Attention-based mechanisms. Why “Temporal Fusion Transformers” are becoming the gold standard for multi-horizon forecasting.
Demo (Energy/Utilities): Using a Transformer model to forecast 24-hour electricity demand across a Smart Grid, identifying peak loads with 98% precision.
Hands-on: Deploying a pre-trained Transformer model for a multi-variate sales forecast, comparing its performance against Day 1 baselines.
Expected Impact: Technical intuition on when to deploy “Heavyweight” DL models vs. “Lightweight” ML models based on compute ROI.

Module 7: GenAI for Synthetic Data & Scenario Simulation

Using Generative Adversarial Networks (GANs) or LLMs to generate “Synthetic Time Series” for stress-testing models where historical data is scarce.
Scenario (Risk Management): Simulating 10,000 “What-If” scenarios for a port congestion crisis using GenAI-driven synthetic data to test supply chain resilience.
Hands-on: “The Stress Test” – Using GenAI to inject “Black Swan” anomalies into a forecast and evaluating the model’s recovery response.
Expected Impact: Proactive risk management and the ability to train models for events that haven’t happened yet.

Module 8: MLOps for Time Series & The 90-Day Roadmap

Handling “Data Drift” and “Concept Drift” in temporal models. Backtesting strategies (Expanding vs. Sliding Window) and automated retraining loops.
The Framework: Prioritizing the “Forecasting Backlog” based on Volatility, Data Quality, and Business Impact.
Hands-on: Setting up a Model Monitoring Dashboard in Python to track “Mean Absolute Percentage Error” (MAPE) degradation in a live environment.
Expected Impact: A clear, sustainable roadmap for moving forecasting models from “Lab” to “Live Production.”

List of Deliverables

Time Series Python Toolkit: A repository of notebooks covering ARIMA, Prophet, LSTMs, and Transformer implementations.
Temporal Feature Engineering Library: A set of GenAI-powered scripts for automated lag and window generation.
PDPA Compliance Manual for DS: A technical guide for securing time-indexed datasets.

Prerequisites

Technical Knowledge: Basic Python proficiency (Pandas/NumPy) and a fundamental understanding of linear algebra and statistics.
Essential Equipment: A laptop with an environment like Anaconda, VS Code, or access to Google Colab.
Mindset: A focus on technical rigor and the understanding that "All models are wrong, but some are useful."

Who Should Attend

Training Methodology

Code-First Architecture: 70% of the program is hands-on coding, model tuning, and backtesting.
Mathematical Deconstruction: Moving beyond "Black Boxes" to understand the weights and biases of temporal models.
Technical Co-Design: Group sessions to solve actual departmental "Time-Data" bottlenecks using advanced DS patterns.

100% HRDC-Claimable

This program is fully registered and compliant with HRDC (Human Resource Development Corporation) requirements under the SBL-Khas scheme, allowing Malaysian employers to offset the training costs against their levy.

Certification of Completion

Participants who successfully complete the program will be awarded a “Professional Certificate in Advanced Time Series Forecasting & AI Orchestration.“

Post-Workshop Consulting (Optional)

For organizations looking to bridge the gap between training and execution, we offer optional, paid consulting services. These engagements provide expertise and technical support for specific pilot development or full-scale operational integration of the data- and AI-driven use cases established during the program.