Time Series Forecasting in Python (Paperback)

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.

In Time Series Forecasting in Python you will learn how to:

Recognize a time series forecasting problem and build a performant predictive model
Create univariate forecasting models that account for seasonal effects and external variables
Build multivariate forecasting models to predict many time series at once
Leverage large datasets by using deep learning for forecasting time series
Automate the forecasting process

Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
You can predict the future--with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before.

About the book
Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you'll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you'll soon be ready to build your own accurate, insightful forecasts.

What's inside

Create models for seasonal effects and external variables
Multivariate forecasting models to predict multiple time series
Deep learning for large datasets
Automate the forecasting process

About the reader
For data scientists familiar with Python and TensorFlow.

About the author
Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks.

Table of Contents
PART 1 TIME WAITS FOR NO ONE
1 Understanding time series forecasting
2 A naive prediction of the future
3 Going on a random walk
PART 2 FORECASTING WITH STATISTICAL MODELS
4 Modeling a moving average process
5 Modeling an autoregressive process
6 Modeling complex time series
7 Forecasting non-stationary time series
8 Accounting for seasonality
9 Adding external variables to our model
10 Forecasting multiple time series
11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia
PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING
12 Introducing deep learning for time series forecasting
13 Data windowing and creating baselines for deep learning
14 Baby steps with deep learning
15 Remembering the past with LSTM
16 Filtering a time series with CNN
17 Using predictions to make more predictions
18 Capstone: Forecasting the electric power consumption of a household
PART 4 AUTOMATING FORECASTING AT SCALE
19 Automating time series forecasting with Prophet
20 Capstone: Forecasting the monthly average retail price of steak in Canada
21 Going above and beyond

從您的數據中建立基於時間模式的預測模型。掌握包括新的深度學習方法在內的統計模型，用於時間序列預測。

在《Python時間序列預測》中，您將學習以下內容：

- 辨識時間序列預測問題並建立高效的預測模型
- 創建考慮季節效應和外部變量的單變量預測模型
- 建立多變量預測模型，同時預測多個時間序列
- 利用深度學習預測時間序列，以應對大型數據集
- 自動化預測過程

《Python時間序列預測》教您如何從基於時間的數據中建立強大的預測模型。您所創建的每個模型都是相關、有用且易於使用的Python工具。您將探索有趣的現實世界數據集，如Google的每日股價和美國的經濟數據，從基礎知識迅速發展到使用TensorFlow等深度學習工具開發大型模型。

購買印刷版書籍將包含Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於技術：
您可以借助Python、深度學習和時間序列數據來預測未來！時間序列預測是一種對時間導向數據進行建模的技術，用於識別即將發生的事件。新的Python庫和強大的深度學習工具使得準確的時間序列預測比以往更容易。

關於本書：
《Python時間序列預測》教您如何從基於時間的數據（如日誌、客戶分析和其他事件流）中獲得即時、有意義的預測。在這本易於理解的書中，您將學習用於時間序列預測的統計和深度學習方法，並通過帶有註釋的Python代碼進行全面演示。通過項目（如預測未來的藥物處方量），提高您的技能，很快就能夠建立準確、有洞察力的預測。

內容簡介：
- 創建考慮季節效應和外部變量的模型
- 預測多個時間序列的多變量預測模型
- 大型數據集的深度學習
- 自動化預測過程

讀者對象：
熟悉Python和TensorFlow的數據科學家。

作者簡介：
Marco Peixeiro是一位經驗豐富的數據科學講師，曾在加拿大最大的銀行之一擔任數據科學家。

目錄：
第1部分 時間不等人
1 理解時間序列預測
2 未來的天真預測
3 隨機遊走
第2部分 使用統計模型進行預測
4 建模移動平均過程
5 建模自回歸過程
6 建模複雜時間序列
7 預測非平穩時間序列
8 考慮季節性
9 在模型中添加外部變量
10 預測多個時間序列
11 綜合項目：預測澳大利亞抗糖尿病藥物處方數量
第3部分 使用深度學習進行大規模預測
12 深度學習介紹時間序列預測
13 數據窗口和深度學習的基線
14 深度學習的初步步驟
15 LSTM記憶過去
16 使用CNN過濾時間序列
17 使用預測進行更多預測
18 綜合項目：預測家庭電力消耗
第4部分 自動化大規模預測
19 使用Prophet自動化時間序列預測
20 綜合項目：預測加拿大牛排的月均零售價格
21 超越預測

Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks. He is an active contributor to Towards Data Science, an instructor on Udemy, and on YouTube in collaboration with freeCodeCamp.

Marco Peixeiro是一位經驗豐富的數據科學講師，曾在加拿大最大的銀行擔任數據科學家。他是Towards Data Science的活躍貢獻者，也是Udemy的講師，並與freeCodeCamp合作在YouTube上提供教學內容。