Time Series Analysis with Python Cookbook: Practical Recipes for Exploratory Data Analysis, Data Preparation, Forecasting, and Model Evaluation
Tarek A Atwan

Informasi Umum

Kode

24.01.378

Klasifikasi

005.133 - Special programming techniques-specific programming language

Jenis

Buku - Circulation (Dapat Dipinjam)

Subjek

Phyton, Time Series Analysis,

No. Rak

Tel-U Gedung Manterawu Lantai 5 : Rak 2
Tel-U Purwokerto : Rak 2

Dilihat

186 kali

Informasi Lainnya

Abstraksi

Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting.

This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you'll work with ML and DL models using TensorFlow and PyTorch.

Finally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.

What You Will Learn:

Understand what makes time series data different from other data Apply various imputation and interpolation strategies for missing data Implement different models for univariate and multivariate time series Use different deep learning libraries such as TensorFlow, Keras, and PyTorch Plot interactive time series visualizations using hvPlot Explore state-space models and the unobserved components model (UCM) Detect anomalies using statistical and machine learning methods Forecast complex time series with multiple seasonal patterns

Who this book is for:

This book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.

• - Time Series Data Analysis

Koleksi & Sirkulasi

Tersedia 1 dari total 1 Koleksi

Pengarang

Nama	Tarek A Atwan
Jenis	Perorangan
Penyunting
Penerjemah

Penerbit

Nama	Packt Publishing
Kota	New York
Tahun	2022

Sirkulasi

Harga sewa	IDR 0,00
Denda harian	IDR 1.000,00
Jenis	Sirkulasi

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