In this chapter, we have described three deep learning-based approaches to develop time series forecasting models. Neural networks are suitable in cases where there is little information about the underlying properties such as long-term trend and seasonality or these are too complex to be modeled with an acceptable degree of accuracy by traditional statistical methods. Different neural network architectures such as MLP, RNN, and CNN extract complex patterns from the data. If neural network models are trained with appropriate measures to avoid overfitting on training data, then these models generalize well on unseen validation or test data. To avoid overfitting, we applied dropout, which is widely used in deep neural networks for a variety of datasets and applications. We hope that this chapter gives you an idea of advanced techniques available for time series forecasting. The Jupyter notebooks accompanying the chapter are expected to give you the necessary base knowledge, which would...
Practical Time Series Analysis
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Practical Time Series Analysis
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Overview of this book
Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python.
The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality, and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.
The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.
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Current Title:
Practical Time Series Analysis
Table of Contents (13 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Introduction to Time Series
Understanding Time Series Data
Exponential Smoothing based Methods
Auto-Regressive Models
Deep Learning for Time Series Forecasting
Customer Reviews