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Leire Ahedo
En este proyecto aplicado y práctico aprenderás a utilizar Prophet y neuralProphet.
Prophet es una de las librerías más avanzadas para predecir series temporales desarrollada por Facebook. Te enseñaremos a como entrenar un modelo con Prophet, a añadir regresores adicionales como periodos vacacionales y variables adicionales, a optimizarlo y a utilizarlo para realizar predicciones futuras.
También aprenderemos a utilizar neuralProphet, que esta basada en modelos de deep learning.
Al finalizar este curso habrás aprendido a entrenar tus propios modelos y a aplicarlos en tus propios proyectos.
Register for this course and see more details by visiting:
OpenCourser.com/course/ojiynv/series
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What's inside
Syllabus
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Dirigido a estudiantes y profesionales que buscan desarrollar habilidades prácticas en el modelado de series temporales
Impartido por Leire Ahedo, una experta en el modelado de series temporales
Utiliza herramientas populares de la industria como Prophet y neuralProphet
Cubre conceptos esenciales como el entrenamiento, la optimización y la predicción de modelos de series temporales
Puede requerir conocimientos previos en estadística y programación
Está diseñado para un nivel intermedio o avanzado en el modelado de series temporales
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Reviews summary
Series temporales con prophet y neuralprophet
Según los estudiantes, este curso ofrece una sólida introducción práctica a la predicción de series temporales utilizando las librerías Facebook Prophet y NeuralProphet. Muchos valoran su enfoque directo y la aplicación inmediata en proyectos profesionales, destacando los ejemplos de código claros y los cuadernos de Jupyter. El instructor es ampliamente elogiado por su claridad y concisión. Si bien es excelente para iniciarse en estas herramientas, algunos usuarios avanzados o sin conocimientos previos en series temporales sugieren que podría faltar mayor profundidad teórica o abordar casos de uso más complejos, requiriendo una base en Python o temas de data science.
Adecuado para introducción, pero superficial para temas avanzados.
"El curso es bueno para una introducción, pero sentí que le faltó un poco de teoría detrás de los modelos. Necesité buscar información adicional."
"Quizás podría profundizar más en la interpretación de los resultados y en la optimización de hiperparámetros."
"Me pareció demasiado superficial. Esperaba más profundidad en la parte de optimización y tuning. Los ejemplos son muy básicos."
El contenido se mantiene relevante y el soporte es efectivo.
"El contenido está actualizado y el instructor se toma el tiempo para explicar cada paso."
"El instructor resuelve dudas en el foro rápidamente. Lo recomiendo para quienes quieren una guía directa y práctica."
"Es un curso que se siente vigente y bien mantenido, lo cual se agradece en este campo."
La instrucción es clara, concisa y fácil de seguir, con buenos recursos.
"El instructor explica muy bien los conceptos. ¡Muy recomendable para profesionales!"
"Increíblemente útil. Siempre quise entender cómo usar Prophet y este curso lo hizo muy accesible. Los cuadernos de Jupyter son una maravilla."
"La explicación de los modelos es muy directa, orientada a la implementación y muy clara."
Destaca por su orientación práctica y aplicabilidad directa.
"Me ayudó a aplicar Prophet y NeuralProphet en mis proyectos de trabajo de inmediato."
"La parte de cómo añadir regresores y holidays fue particularmente útil para mi caso de uso real."
"Excelente material, muy enfocado a la aplicación práctica. La combinación de Prophet y NeuralProphet es muy potente."
El ritmo puede ser rápido sin experiencia previa en series temporales.
"Siento que el ritmo fue un poco rápido para alguien sin mucha experiencia previa en series temporales. Necesitaría un curso pre-requisito."
"Si ya tienes algo de base en Python y Pandas, te resultará muy sencillo seguirlo."
"Para aprovecharlo bien, es mejor tener una base en programación y análisis de datos."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Series temporales con Facebook’ Prophet y NeuralProphet with these
activities:
Organiza tus apuntes y materiales de clase
Show steps
Organizar tus apuntes y materiales de clase te ayudará a acceder a la información de manera más fácil y a prepararte mejor para las evaluaciones.
Show steps
-
Recopila todos tus apuntes, materiales de clase y otros recursos
-
Crea un sistema de organización para tus materiales
-
Archiva tus materiales de forma que sean fáciles de encontrar
Sigue tutoriales de neuralProphet
Show steps
Seguir tutoriales sobre neuralProphet te ayudará a complementar tus conocimientos sobre modelos de predicción de series temporales basados en deep learning.
Show steps
-
Busca tutoriales de neuralProphet online o en plataformas de aprendizaje
-
Sigue los tutoriales paso a paso
-
Prueba los ejemplos proporcionados en los tutoriales
Resuelve ejercicios prácticos de Prophet
Show steps
Realizar ejercicios prácticos te ayudará a reforzar tu comprensión de Prophet y a mejorar tus habilidades para utilizarlo.
Browse courses on
Prophet
Show steps
-
Busca ejercicios prácticos online o en libros de texto
-
Intenta resolver los ejercicios por tu cuenta
-
Comprueba tus respuestas con las soluciones proporcionadas
Two other activities
Expand to see all activities and additional details
Show all five activities
Escribe un resumen de los conceptos clave de Prophet
Show steps
Resumir los conceptos clave de Prophet te ayudará a consolidar tu comprensión y a identificar las áreas que necesitas reforzar.
Browse courses on
Prophet
Show steps
-
Revisa tus notas y materiales de clase
-
Identifica los conceptos clave de Prophet
-
Escribe un resumen claro y conciso de cada concepto
Crea un modelo de predicción con Prophet
Show steps
Crear un modelo de predicción de series temporales con Prophet te permitirá aplicar tus conocimientos y habilidades en un proyecto práctico.
Browse courses on
Prophet
Show steps
-
Recopila y prepara un conjunto de datos de series temporales
-
Entrena un modelo Prophet con los datos preparados
-
Evalúa el rendimiento del modelo con métricas apropiadas
-
Haz predicciones con el modelo
Organiza tus apuntes y materiales de clase
Show steps
Organizar tus apuntes y materiales de clase te ayudará a acceder a la información de manera más fácil y a prepararte mejor para las evaluaciones.
Show steps
Show steps
Show steps
- Recopila todos tus apuntes, materiales de clase y otros recursos
- Crea un sistema de organización para tus materiales
- Archiva tus materiales de forma que sean fáciles de encontrar
Sigue tutoriales de neuralProphet
Show steps
Seguir tutoriales sobre neuralProphet te ayudará a complementar tus conocimientos sobre modelos de predicción de series temporales basados en deep learning.
Show steps
Show steps
Show steps
- Busca tutoriales de neuralProphet online o en plataformas de aprendizaje
- Sigue los tutoriales paso a paso
- Prueba los ejemplos proporcionados en los tutoriales
Resuelve ejercicios prácticos de Prophet
Show steps
Realizar ejercicios prácticos te ayudará a reforzar tu comprensión de Prophet y a mejorar tus habilidades para utilizarlo.
Browse courses on
Prophet
Show steps
Show steps
Show steps
- Busca ejercicios prácticos online o en libros de texto
- Intenta resolver los ejercicios por tu cuenta
- Comprueba tus respuestas con las soluciones proporcionadas
Two other activities
Expand to see all activities and additional details
Show all five activities
Escribe un resumen de los conceptos clave de Prophet
Show steps
Resumir los conceptos clave de Prophet te ayudará a consolidar tu comprensión y a identificar las áreas que necesitas reforzar.
Browse courses on
Prophet
Show steps
Show steps
Show steps
- Revisa tus notas y materiales de clase
- Identifica los conceptos clave de Prophet
- Escribe un resumen claro y conciso de cada concepto
Crea un modelo de predicción con Prophet
Show steps
Crear un modelo de predicción de series temporales con Prophet te permitirá aplicar tus conocimientos y habilidades en un proyecto práctico.
Browse courses on
Prophet
Show steps
Show steps
Show steps
- Recopila y prepara un conjunto de datos de series temporales
- Entrena un modelo Prophet con los datos preparados
- Evalúa el rendimiento del modelo con métricas apropiadas
- Haz predicciones con el modelo
Career center
Learners who complete Series temporales con Facebook’ Prophet y NeuralProphet will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. This course may be useful for Data Scientists who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve business problems. This course may be useful for Machine Learning Engineers who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Machine Learning Engineers.
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes data to help businesses make informed decisions. This course may be useful for Data Analysts who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Data Analysts.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical methods to analyze data and make investment decisions. This course may be useful for Quantitative Analysts who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for financial time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Quantitative Analysts.
Financial Analyst
Financial Analyst
A Financial Analyst evaluates and interprets financial data to make investment recommendations. This course may be useful for Financial Analysts who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for financial time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Financial Analysts.
Actuary
Actuary
An Actuary uses mathematical and statistical methods to assess risk and uncertainty. This course may be useful for Actuaries who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for insurance time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Actuaries.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical methods to solve business problems. This course may be useful for Operations Research Analysts who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Operations Research Analysts.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and recommends solutions to improve efficiency. This course may be useful for Business Analysts who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Business Analysts.
Market Researcher
Market Researcher
A Market Researcher conducts research to understand consumer behavior and market trends. This course may be useful for Market Researchers who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Market Researchers.
Product Manager
Product Manager
A Product Manager is responsible for the planning, development, and launch of new products. This course may be useful for Product Managers who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Product Managers.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for the planning and execution of marketing campaigns. This course may be useful for Marketing Managers who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Marketing Managers.
Sales Manager
Sales Manager
A Sales Manager is responsible for the planning and execution of sales strategies. This course may be useful for Sales Managers who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Sales Managers.
Customer Success Manager
Customer Success Manager
A Customer Success Manager is responsible for ensuring that customers are satisfied with their products and services. This course may be useful for Customer Success Managers who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Customer Success Managers.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and infrastructure. This course may be useful for Data Engineers who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Data Engineers.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful for Software Engineers who want to learn how to use Prophet and neuralProphet to build and deploy predictive models for time series data. The course covers topics such as data preprocessing, model training, evaluation, and deployment, which are all essential skills for Software Engineers.
For more career information including salaries, visit:
OpenCourser.com/course/ojiynv/series
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Series temporales con Facebook’ Prophet y NeuralProphet.
Save
Provides a comprehensive overview of forecasting methods, including time series analysis, regression, and machine learning. It useful reference for practitioners and researchers in various fields.
Este libro proporciona una guía práctica para el análisis de datos de series temporales utilizando el software R. Cubre técnicas esenciales para el manejo, procesamiento y visualización de datos de series temporales.
Save
Provides a detailed overview of the Box-Jenkins approach to time series analysis and forecasting. It covers a wide range of topics, including data exploration, model selection, and forecasting evaluation.
Provides a broad overview of time series prediction techniques, including both classical and modern methods. It covers a wide range of topics, including data exploration, model selection, and forecasting evaluation.
Save
Provides a comprehensive introduction to time series analysis using R software. It covers a wide range of topics, including data exploration, model selection, and forecasting evaluation.
Provides a comprehensive introduction to time series analysis and forecasting, covering both theoretical and practical aspects. It valuable resource for anyone interested in learning more about time series analysis and forecasting.
Provides a comprehensive introduction to time series analysis using R software. It covers a wide range of topics, including data exploration, model selection, and forecasting evaluation.
Provides a comprehensive introduction to time series analysis using R software. It covers a wide range of topics, including data exploration, model selection, and forecasting evaluation.
Save
Provides a theoretical foundation for time series analysis and forecasting. It good resource for learners who want to understand the underlying concepts and mathematical models used in this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ojiynv/series
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