Forecasting Models An Overview With The Help Of R Software
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Forecasting models – an overview with the help of R software
Author: Editor IJSMI
language: en
Publisher: international Journal of Statistics and Medical Informatics
Release Date: 2019-07-20
Forecasting models – an overview with the help of R software Preface Forecasting models involves predicting the future values of a particular series of data which is mainly based on the time domain. Forecasting models are widely used in the fields such as financial markets, demand for a product and disease outbreak. The objective of the forecasting model is to reduce the error in the forecasting. Most of the Forecasting models are based on time series, a statistical concept which involves Moving Averages, Auto Regressive Integrated Moving Averages (ARIMA), Exponential smoothing and Generalized Auto Regressive Conditional Heteroscedastic (GARCH) Models. Forecasting models which we deal in this book will be explorative forecasting models which take into account the past data to predict the future values. Current day forecasting models uses advanced techniques such as Machine Learning and Deep Learning Algorithms which are more robust and can handle high volume of data. This book starts with the overview of forecasting and time series concepts and moves on to build forecasting models using different time series models. Examples related to forecasting models which are built based on Machine learning also covered. The book uses R statistical software package, an open source statistical package to build the forecasting models. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php https://www.amazon.co.uk/dp/B07VFY53B1
Forecasting: principles and practice
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Deep Learning Models explored with help of Python Programming
Author: Editor IJSMI
language: en
Publisher: International Journal of Statistics and Medical Informatics
Release Date: 2020-11-04
This is the second book in the Deep Learning models series by the author. Deep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models. The book starts with the Introduction part which is adopted from Author’s Deep Learning Models and its application: An overview with the help of R software book and move on to the Python’s important data processing packages such Numpy, and Pandas. Book then explores the Deep Learning models with the help of packages such as Pytorch, Tensor Flow and Keras and their applications in image processing, stock market prediction, recommender systems and natural language processing. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php ISBN: 9798558877953 E-Books: https://www.amazon.com/dp/B08MQTM1ZP Paperbacks: https://www.amazon.com/dp/B08MSQ3R8R