Название: Time Series with Python: A Beginner’s Guide. 2019 Edition
Автор: Jim Smith
Издательство: Jim Smith Publishing
Год: 2019
Страниц: 55
Язык: английский
Формат: epub, azw3, rtf, pdf (conv)
Размер: 10.08 MB

A time series is a sequence of observations over a certain period. The simplest example of a time series that all of us come across on a day to day basis is the change in temperature throughout the day or week or month or year. The analysis of temporal data is capable of giving us useful insights on how a variable changes over time.

Time series models are very useful models when you have serially correlated data. Most of business houses work on time series data to analyze sales number for the next year, website traffic, competition position and much more. However, it is also one of the areas, which many analysts do not understand. So, if you aren’t sure about complete process of time series modeling, this guide would introduce you to various levels of time series modeling and its related techniques.

This book will teach you how to analyze and forecast time series data with the help of various statistical and machine learning models in elaborate and easy to understand way!

To whom this tutorial is designed for:
This book is for those who are looking to understand time series and time series forecasting models from scratch. At the end of this book you will have a good understanding on time series modelling.

Prerequisites :
This tutorial only assumes a preliminary understanding of Python language. Although this tutorial is self-contained, it will be useful if you have understanding of statistical mathematics. If you are new to either Python or Statistics, we suggest you to pick up a tutorial based on these subjects first before you embark on your journey with Time Series.

What you will Learn:
• Introduction
• Data Processing and Visualization
• Modeling
• Parameter Calibration
• Naïve Methods
• Moving Average
• ARIMA
• Exponential Smoothing
• Walk Forward Validation
• Prophet Model
• LSTM Model
• Error Metrics
• Applications








[related-news] [/related-news]