К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Deep Learning for Time Series Cookbook · LearnSpace
Назад в каталог
courseraАнализ данных

Deep Learning for Time Series Cookbook

Курс от Packt
Средний≈ 7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Deep Learning for Time Series Cookbook is a hands-on course that helps you tackle a variety of time series problems using deep learning through practical coding recipes. You'll learn how to develop accurate forecasting models and extract insights from temporal data using the PyTorch ecosystem. Throughout this course, you'll explore essential concepts and architectures, including CNNs, transformers, autoencoders, and PyTorch Lightning, gaining practical experience in building models for forecasting, classification, and anomaly detection. The step-by-step recipes guide you from preprocessing time series data to creating production-ready predictive solutions. The course emphasizes real-world applications, showing how deep learning can uncover complex patterns, improve predictions, and optimize decision-making for univariate and multivariate datasets. Each module blends theory with hands-on exercises to reinforce understanding and ensure skills are immediately applicable. This course is ideal for machine learning enthusiasts, data scientists, and AI professionals looking to enhance their skills in time series analysis. A basic knowledge of Python and foundational machine learning concepts is recommended to get the most out of the content.

Навыки, которые вы освоите

ForecastingAutoencodersAnomaly DetectionTime Series Analysis and ForecastingUnsupervised LearningPredictive AnalyticsDeep LearningExploratory Data AnalysisPyTorch (Machine Learning Library)Model OptimizationRecurrent Neural Networks (RNNs)Data PreprocessingApplied Machine LearningGenerative Model ArchitecturesModel TrainingData ArchitectureArtificial Neural NetworksConvolutional Neural NetworksModel EvaluationPredictive Modeling

Программа курса

9 модулей · 74 учебных материалов

01Getting Started with Time Series8 материалов

Unveiling Patterns: Visualizing and Decomposing Time Series Data

OverviewВидеоIntroductionЧтениеVisualizing a Time SeriesЧтениеGetting ReadyЧтение

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Deep Learning for Time Series Cookbook
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 7 ч

9 модулей

Язык: Английский

Часть программы вашего университета
Decomposing a Time SeriesЧтение
Computing AutocorrelationЧтение
Dealing with HeteroskedasticityЧтение
Foundations of Time Series AnalysisЗадание
02Getting Started with PyTorch7 материалов

Hands-On Neural Network Foundations with PyTorch

OverviewВидеоIntroductionЧтениеBasic Operations in PyTorchЧтениеBuilding a Simple Neural Network with PyTorchЧтениеTraining a Recurrent Neural NetworkЧтениеTraining an LSTM Neural NetworkЧтениеFoundations of Deep Learning with PyTorchЗадание
03Univariate Time Series Forecasting13 материалов

Mastering Univariate Forecasting: From ARIMA to Deep Learning

OverviewВидеоIntroductionЧтениеUnivariate Forecasting with ARIMAЧтениеPreparing a Time Series for Supervised LearningЧтениеUnivariate Forecasting with a Feedforward Neural NetworkЧтениеUnivariate Forecasting with an LSTMЧтениеUnivariate Forecasting with a GRUЧтениеCombining an LSTM with Multiple Fully Connected LayersЧтениеHandling Trend Taking First DifferencesЧтениеHandling Seasonality Seasonal Dummies and Fourier SeriesЧтениеHandling Seasonality Seasonal DifferencingЧтениеHandling Non-Constant Variance Log TransformationЧтениеTime Series Forecasting FundamentalsЗадание
04Forecasting with PyTorch Lightning8 материалов

Building and Evaluating Deep Learning Forecasts for Time Series Data

OverviewВидеоIntroductionЧтениеUsing the TimeSeriesDataSet ClassЧтениеTraining a Linear Regression Model for Forecasting with a Multivariate Time SeriesЧтениеFeedforward Neural Networks for Multivariate Time Series ForecastingЧтениеLSTM Neural Networks for Multivariate Time Series ForecastingЧтениеMonitoring the Training Process Using TensorBoardЧтениеPyTorch Lightning and Time Series Forecasting FundamentalsЗадание
05Global Forecasting Models8 материалов

Mastering Deep Learning for Complex Time Series Forecasting

OverviewВидеоIntroductionЧтениеMulti-step and Multi-output Forecasting with Multivariate Time SeriesЧтениеPreparing Multiple Time Series for a Global ModelЧтениеTraining a Global LSTM with Multiple Time SeriesЧтениеGlobal Forecasting Models for Seasonal Time SeriesЧтениеHyperparameter Optimization Using Ray TuneЧтениеGlobal Forecasting Models and TechniquesЗадание
06Advanced Deep Learning Architectures for Time Series Forecasting8 материалов

Mastering State-of-the-Art Models for Time Series Prediction

OverviewВидеоIntroductionЧтениеOptimizing the Learning Rate with PyTorch ForecastingЧтениеTraining a DeepAR Model with GluonTSЧтениеGetting ReadyЧтениеTraining a Temporal Fusion Transformer with GluonTSЧтениеTraining an Informer Model with NeuralForecastЧтениеAdvanced Deep Learning Models for Time Series PredictionЗадание
07Probabilistic Time Series Forecasting8 материалов

Quantifying Uncertainty: Advanced Methods in Time Series Forecasting

OverviewВидеоIntroductionЧтениеExceedance Probability Forecasting with an LSTMЧтениеCreating Prediction Intervals Using Conformal PredictionЧтениеProbabilistic Forecasting with an LSTMЧтениеProbabilistic Forecasting with DeepARЧтениеIntroduction to Gaussian ProcessesЧтениеProbabilistic Forecasting FundamentalsЗадание
08Deep Learning for Time Series Classification7 материалов

Mastering Neural Networks and Frameworks for Time Series Data

OverviewВидеоIntroductionЧтениеBuilding a DataModule Class for TSCЧтениеConvolutional Neural Networks for TSCЧтениеResNets for TSCЧтениеTackling TSC Problems with SktimeЧтениеDeep Learning for Time Series Classification ConceptsЗадание
09Deep Learning for Time Series Anomaly Detection7 материалов

Uncovering Anomalies: Deep Learning Approaches for Time Series Data

OverviewВидеоIntroductionЧтениеPrediction-Based Anomaly Detection Using DLЧтениеAnomaly Detection Using an LSTM AEЧтениеBuilding an AE Using PyODЧтениеCreating a VAE for Time Series Anomaly DetectionЧтениеDeep Learning Techniques for Time Series Anomaly DetectionЗадание