- Добавил: literator
- Дата: 21-11-2022, 05:29
- Комментариев: 0
Название: Modeling and Advanced Techniques in Modern Economics
Автор: Cagdas Hakan Aladag, Nihan Potas
Издательство: World Scientific Publishing
Год: 2022
Страниц: 328
Язык: английский
Формат: pdf (true)
Размер: 15.8 MB
In the modern world, data is a vital asset for any organization, regardless of industry or size. The world is built upon data. However, data without knowledge is useless. The aim of this book, briefly, is to introduce new approaches that can be used to shape and forecast the future by combining the two disciplines of Statistics and Economics. Readers of Modeling and Advanced Techniques in Modern Economics can find valuable information from a diverse group of experts on topics such as finance, econometric models, stochastic financial models and machine learning, and application of models to financial and macroeconomic data. The readers can also find useful information on advanced time series forecasting techniques, such as artificial neural networks, Deep Learning, Machine Learning and chaotic time series. An example of time series forecasting with Python code and results for LSTM are given.
Автор: Cagdas Hakan Aladag, Nihan Potas
Издательство: World Scientific Publishing
Год: 2022
Страниц: 328
Язык: английский
Формат: pdf (true)
Размер: 15.8 MB
In the modern world, data is a vital asset for any organization, regardless of industry or size. The world is built upon data. However, data without knowledge is useless. The aim of this book, briefly, is to introduce new approaches that can be used to shape and forecast the future by combining the two disciplines of Statistics and Economics. Readers of Modeling and Advanced Techniques in Modern Economics can find valuable information from a diverse group of experts on topics such as finance, econometric models, stochastic financial models and machine learning, and application of models to financial and macroeconomic data. The readers can also find useful information on advanced time series forecasting techniques, such as artificial neural networks, Deep Learning, Machine Learning and chaotic time series. An example of time series forecasting with Python code and results for LSTM are given.