| Best Sellers Rank | #399,296 in Books (See Top 100 in Books) #80 in Data Processing #359 in Python Programming #407 in Probability & Statistics (Books) |
| Customer Reviews | 4.3 out of 5 stars 30Reviews |
Real-world applications of time series forecasting span a wide range of fields and industries including supply chain management, econometrics, retail forecasting, environmental studies, and healthcare, among many others.
In Time Series Forecasting in Python, you’ll learn to build powerful predictive models from time-based data, starting with the basics and quickly advancing to developing large-scale models using Python, TensorFlow, and other modern deep learning tools.
Imagine…
Time Series Forecasting in Python stands out from other books on this subject by showcasing how Python can be used for both machine learning and statistical approaches to time series analysis.
While many time series forecasting solutions have been implemented in R (a language made for statistical computing), data scientists are generally proficient in Python: the most predominant language in the machine learning field.
Time Series Forecasting in Python stands out compared to other books on this subject as it showcases how modern technology such as TensorFlow and Python can be used for time series analysis.
Thanks to Python’s versatility, we can develop models, perform statistical tests, and develop a web interface using the same programming language.
This book is written for data scientists or analysts with at least one year of experience, perhaps already working in R and looking to transition to Python. No prior experience in time series analysis is required.
