Predictive Power: A Complete Course on Time-Series Forecasting with ML

Introduction

Time-series forecasting is a critical skill in today's data-driven world, enabling businesses to predict future trends and make informed decisions. From anticipating sales to forecasting stock prices and predicting energy consumption, the ability to analyze and model sequential data is a powerful asset. While traditional statistical methods have long been the standard, machine learning offers more flexible and robust approaches to handle complex patterns and large datasets.

This five-day training course is designed to equip you with the knowledge and practical skills to build powerful forecasting models using modern machine learning techniques. You will learn to prepare time-series data, apply advanced models, and evaluate their performance. By the end of this course, you will be able to tackle a wide range of forecasting challenges and deliver accurate, actionable predictions for your organization.

Duration 5 days

Target Audience This course is for data analysts, data scientists, and machine learning engineers who want to specialize in time-series forecasting. It is assumed that participants have a basic understanding of Python and machine learning concepts.

Objectives

  1. To understand the fundamental concepts of time-series data and its unique characteristics.
  2. To master the art of data preprocessing and feature engineering for time-series.
  3. To learn and apply various statistical forecasting models as a baseline.
  4. To build and evaluate forecasting models using traditional machine learning algorithms.
  5. To explore the power of deep learning for complex time-series patterns.
  6. To implement a complete forecasting pipeline, from data ingestion to model deployment.
  7. To understand and quantify model accuracy and error metrics.
  8. To handle and forecast time-series data with multiple variables (multivariate).
  9. To learn strategies for handling seasonal, trend, and cyclical components.
  10. To gain experience with popular libraries like Pandas, scikit-learn, and TensorFlow/PyTorch.

Course Modules

Module 1: Time-Series Fundamentals

  • What makes time-series data unique?
  • Components of a time-series: trend, seasonality, and cycles.
  • Stationarity: what it is and why it's important.
  • Techniques for visualizing time-series data.
  • A review of basic time-series concepts.

Module 2: Data Preparation & Feature Engineering

  • Handling missing values in time-series data.
  • Resampling and aggregation techniques.
  • Creating lagged features from historical data.
  • Extracting time-based features (e.g., day of week, month).
  • The concept of rolling statistics and window functions.

Module 3: Classical Forecasting Methods

  • Naive and seasonal naive forecasting.
  • Exponential smoothing methods.
  • The basics of ARIMA, SARIMA, and SARIMAX models.
  • Autocorrelation and partial autocorrelation plots.
  • Applying and interpreting statistical models.

Module 4: Machine Learning for Forecasting

  • Framing a forecasting problem as a supervised learning task.
  • Using linear regression as a forecasting tool.
  • An introduction to Tree-based models: Random Forests and Gradient Boosting Machines.
  • Applying scikit-learn to build time-series models.
  • Advantages and disadvantages of using ML models over statistical models.

Module 5: Deep Learning for Forecasting

  • The power of Recurrent Neural Networks (RNNs).
  • An introduction to Long Short-Term Memory (LSTM) networks.
  • Using Convolutional Neural Networks (CNNs) for pattern recognition.
  • A hands-on guide to building and training a deep learning model.
  • The role of deep learning in handling complex, non-linear patterns.

Module 6: Univariate vs. Multivariate Forecasting

  • Forecasting with a single variable.
  • The concept of exogenous variables.
  • Integrating multiple related variables into your model.
  • Creating features from multivariate data.
  • A discussion on the benefits and challenges of multivariate forecasting.

Module 7: Model Validation and Evaluation

  • Common time-series validation strategies.
  • The importance of a proper backtesting framework.
  • Key error metrics: MAE, RMSE, MAPE.
  • Using plots to visualize and compare forecasts.
  • A step-by-step guide to evaluating model performance.

Module 8: Hyperparameter Tuning

  • Understanding what a hyperparameter is.
  • Manual and automated hyperparameter search.
  • Using Grid Search and Random Search to find the best model.
  • A discussion on using automated tools like Optuna.
  • The impact of hyperparameters on model performance.

Module 9: Working with Irregular Data

  • Handling unevenly spaced timestamps.
  • Techniques for data imputation and interpolation.
  • Forecasting at different frequencies (e.g., hourly, daily).
  • A discussion on the challenges of irregularly sampled data.
  • Practical solutions for real-world datasets.

Module 10: Advanced Forecasting Techniques

  • Prophet from Facebook for handling business data.
  • An introduction to neural prophet.
  • Using Lag-based features to improve model accuracy.
  • The concept of feature importance in time-series.
  • A discussion on ensemble methods for forecasting.

Module 11: Real-World Case Studies

  • A case study on forecasting sales for a retail company.
  • A case study on predicting energy consumption.
  • A case study on financial market forecasting.
  • An exploration of demand forecasting in a supply chain.
  • A discussion on the lessons learned from each case.

Module 12: Building a Forecasting Pipeline

  • The end-to-end workflow for a time-series project.
  • Loading and cleaning the data from a source.
  • Building and training the model.
  • Generating forecasts and saving them to a file.
  • A conceptual overview of deploying a forecasting model.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Predictive Power: A Complete Course On Time-series Forecasting With Ml in Namibia
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