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· 제목 : Hands-On Time Series Analysis with Python: From Basics to Bleeding Edge Techniques (Paperback)
· 분류 : 외국도서 > 컴퓨터 > 프로그래밍 언어 > Python
· ISBN : 9781484259917
· 쪽수 : 407쪽
· 출판일 : 2020-08-25
· 분류 : 외국도서 > 컴퓨터 > 프로그래밍 언어 > Python
· ISBN : 9781484259917
· 쪽수 : 407쪽
· 출판일 : 2020-08-25
목차
Chapter 1: Time Series and its Characteristics
Chapter Goal: Basics of time series and its components
? Introduction to time series
? Trend
? Seasonality
? Cyclic
? Univariate data
? Multivariate data
Chapter 2: Time Series Data Wrangling and Visualization
Chapter Goal: Learn data wrangling for time series
? Crafting time series data with pandas
? Time resampling, shifting, and windowing? Handling outliers
? Rolling and expanding
? Internal structures of time series
? Making time series stationary
? Time series decomposition
? Plotting time series data
? Performance evaluation techniques
Chapter 3: Traditional Techniques of Time Series
Chapter Goal: Learn traditional techniques and ways to build traditional models using StatsModel, pyramid, PyFlux.
Introduction traditional techniques
? Smoothening methods
? Introduction Simple Exponential Smoothing
? Double exponential smoothing
? Triple Exponential Smoothing
? VAR
? VARMA
? GARCH
Chapter 4: Regression Extensions Techniques
Chapter Goal: Learn regression extensions techniques and ways to build regression extensions models using StatsModel, pyramid.
? Introduction to AUTO ARIMA using pyramid
ARMAARIMA Assumptions
ARIMA
SARIMA
SARIMAX adding exogenous into models
Chapter 5: Bleeding Edge Techniques
Chapter Goal: Learn to built time series models using ANN, CNN, GRU, LSTM, fbprophet, sktime, and how to use Keras TimeseriesGenerator
? Time series classification with sktime
Introduction to Neural Network
? Introduction ANN
? Introduction CNN
? Introduction GRU
? Introduction LSTM
Chapter 6 :Keras TimeseriesGenerator
Chapter Goal: Learn how to leverage Keras TimeseriesGenerator using which task of data preparation for CNN, GRU and LSTM becomes easier.
Modelling using TimeseriesGenerator
TimeseriesGenerator with CNN
TimeseriesGenerator with LSTMTimeseriesGenerator wit GRU
Chapter 7 : fbprophet
Chapter Goal: Learn how to use one of the most popular time series framework introduced by facebook.
Introduction
Modelling using fbprophet
