Modeling Time-series Data with Deep learning

  • IntermediateLevel

  • 131+Students Enrolled

  • 1 HrDuration

  • 4.8Average Rating

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About this Course

  • Learn how to build model time-series data using deep learning, starting from raw energy data and progressing to neural networks and LSTM-based forecasting models.
  • Build strong forecasting foundations by creating baseline models, applying proper evaluation metrics, and understanding how deep learning improves performance.
  • Gain hands-on experience with feature engineering, windowing strategies, scaling, and training recurrent models to capture temporal patterns.
  • Move from experimentation to practice by evaluating errors, comparing forecasts, and exporting models for reusable real-world inference.

Learning Outcomes

Time-Series Foundations

Understand patterns, seasonality, baselines, and evaluation metrics

Deep Learning Forecasting

Build, train, and compare neural networks and LSTM models

Model Evaluation & Use

Analyze errors, visualize results, and deploy forecasting tools

Who Should Enroll

  • Data science learners looking to apply deep learning techniques to real-world time-series forecasting problems.
  • Machine learning practitioners who want hands-on experience with LSTMs for sequential and temporal data.
  • Analysts and engineers interested in energy forecasting, demand prediction, or time-series modeling applications.

Course Curriculum

Explore energy time-series data, build baselines, engineer features, train neural networks and LSTMs, evaluate forecasts, deploy models, and complete a mini project with real-world extensions.

tools

  1. 1. The Energy Forecasting Challenge

  2. 2. Before We Begin: Pre-Requisite Knowledge

  1. 1. Exploring Energy Data

  2. 2. Baseline Forecasts

  3. 3. How Good is “Naïve”?

  4. 4. From Series to Windows

  1. 1. Our First Neural Network

  2. 2. Forecasting with LSTMs

  1. 1. Measuring Success

  2. 2. From Model to Tool

  3. 3. Before & After Forecasting

  4. 4. Where to Go Next

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Lisa Stuart

Staff Data Scientist

Lisa Stuart is a certified Data Scientist with 6+ years of experience in Python, R, SQL, and BI tools, specializing in predictive analytics, machine learning, time-series modeling, and real-world forecasting using cloud and big data platforms.

Get this Course Now

With this course you’ll get

  • 1 Hour

    Duration

  • Lisa Stuart

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

Time-series forecasting involves predicting future values based on historical, time-ordered data. It focuses on learning temporal patterns such as trends, seasonality, and cycles that evolve sequentially rather than treating data points as independent observations.

Deep learning models like neural networks and LSTMs can capture complex temporal dependencies and non-linear patterns that traditional statistical models often miss, making them effective for large-scale, high-frequency, and multivariate time-series forecasting tasks.

Time-series data has an inherent order and temporal dependency between observations. Shuffling data breaks its structure, so techniques like windowing, sequence modeling, and temporal validation are required instead of standard train-test splits.

Baseline models provide simple reference points to evaluate forecasting performance. Comparing deep learning models against naïve or seasonal baselines helps determine whether added complexity genuinely improves prediction accuracy.

Models are evaluated using time-series metrics such as MAE, RMSE, and MAPE, along with visual error analysis. This helps interpret model reliability across time periods and understand where predictions succeed or fail.

Windowing converts a continuous time-series into supervised learning samples by creating fixed-length input sequences and corresponding future targets. This allows deep learning models to learn temporal relationships from past observations to predict future values.

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