Time series forecasting in Python
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 m...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Shelter Island, New York :
Manning Publications Co
[2022]
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| Edición: | [First edition] |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009701328306719 |
Tabla de Contenidos:
- Intro
- inside front cover
- Time Series Forecasting in Python
- Copyright
- dedication
- contents
- front matter
- preface
- acknowledgments
- about this book
- Who should read this book?
- How this book is organized: A roadmap
- About the code
- liveBook discussion forum
- Author online
- about the author
- about the cover illustration
- Part 1. Time waits for no one
- 1 Understanding time series forecasting
- 1.1 Introducing time series
- 1.1.1 Components of a time series
- 1.2 Bird's-eye view of time series forecasting
- 1.2.1 Setting a goal
- 1.2.2 Determining what must be forecast to achieve your goal
- 1.2.3 Setting the horizon of the forecast
- 1.2.4 Gathering the data
- 1.2.5 Developing a forecasting model
- 1.2.6 Deploying to production
- 1.2.7 Monitoring
- 1.2.8 Collecting new data
- 1.3 How time series forecasting is different from other regression tasks
- 1.3.1 Time series have an order
- 1.3.2 Time series sometimes do not have features
- 1.4 Next steps
- Summary
- 2 A naive prediction of the future
- 2.1 Defining a baseline model
- 2.2 Forecasting the historical mean
- 2.2.1 Setup for baseline implementations
- 2.2.2 Implementing the historical mean baseline
- 2.3 Forecasting last year's mean
- 2.4 Predicting using the last known value
- 2.5 Implementing the naive seasonal forecast
- 2.6 Next steps
- Summary
- 3 Going on a random walk
- 3.1 The random walk process
- 3.1.1 Simulating a random walk process
- 3.2 Identifying a random walk
- 3.2.1 Stationarity
- 3.2.2 Testing for stationarity
- 3.2.3 The autocorrelation function
- 3.2.4 Putting it all together
- 3.2.5 Is GOOGL a random walk?
- 3.3 Forecasting a random walk
- 3.3.1 Forecasting on a long horizon
- 3.3.2 Forecasting the next timestep
- 3.4 Next steps
- 3.5 Exercises
- 3.5.1 Simulate and forecast a random walk.
- 3.5.2 Forecast the daily closing price of GOOGL
- 3.5.3 Forecast the daily closing price of a stock of your choice
- Summary
- Part 2. Forecasting with statistical models
- 4 Modeling a moving average process
- 4.1 Defining a moving average process
- 4.1.1 Identifying the order of a moving average process
- 4.2 Forecasting a moving average process
- 4.3 Next steps
- 4.4 Exercises
- 4.4.1 Simulate an MA(2) process and make forecasts
- 4.4.2 Simulate an MA(q) process and make forecasts
- Summary
- 5 Modeling an autoregressive process
- 5.1 Predicting the average weekly foot traffic in a retail store
- 5.2 Defining the autoregressive process
- 5.3 Finding the order of a stationary autoregressive process
- 5.3.1 The partial autocorrelation function (PACF)
- 5.4 Forecasting an autoregressive process
- 5.5 Next steps
- 5.6 Exercises
- 5.6.1 Simulate an AR(2) process and make forecasts
- 5.6.2 Simulate an AR(p) process and make forecasts
- Summary
- 6 Modeling complex time series
- 6.1 Forecasting bandwidth usage for data centers
- 6.2 Examining the autoregressive moving average process
- 6.3 Identifying a stationary ARMA process
- 6.4 Devising a general modeling procedure
- 6.4.1 Understanding the Akaike information criterion (AIC)
- 6.4.2 Selecting a model using the AIC
- 6.4.3 Understanding residual analysis
- 6.4.4 Performing residual analysis
- 6.5 Applying the general modeling procedure
- 6.6 Forecasting bandwidth usage
- 6.7 Next steps
- 6.8 Exercises
- 6.8.1 Make predictions on the simulated ARMA(1,1) process
- 6.8.2 Simulate an ARMA(2,2) process and make forecasts
- Summary
- 7 Forecasting non-stationary time series
- 7.1 Defining the autoregressive integrated moving average model
- 7.2 Modifying the general modeling procedure to account for non-stationary series.
- 7.3 Forecasting a non-stationary times series
- 7.4 Next steps
- 7.5 Exercises
- 7.5.1 Apply the ARIMA(p,d,q) model on the datasets from chapters 4, 5, and 6
- Summary
- 8 Accounting for seasonality
- 8.1 Examining the SARIMA(p,d,q)(P,D,Q)m model
- 8.2 Identifying seasonal patterns in a time series
- 8.3 Forecasting the number of monthly air passengers
- 8.3.1 Forecasting with an ARIMA(p,d,q) model
- 8.3.2 Forecasting with a SARIMA(p,d,q)(P,D,Q)m model
- 8.3.3 Comparing the performance of each forecasting method
- 8.4 Next steps
- 8.5 Exercises
- 8.5.1 Apply the SARIMA(p,d,q)(P,D,Q)m model on the Johnson &
- Johnson dataset
- Summary
- 9 Adding external variables to our model
- 9.1 Examining the SARIMAX model
- 9.1.1 Exploring the exogenous variables of the US macroeconomics dataset
- 9.1.2 Caveat for using SARIMAX
- 9.2 Forecasting the real GDP using the SARIMAX model
- 9.3 Next steps
- 9.4 Exercises
- 9.4.1 Use all exogenous variables in a SARIMAX model to predict the real GDP
- Summary
- 10 Forecasting multiple time series
- 10.1 Examining the VAR model
- 10.2 Designing a modeling procedure for the VAR(p) model
- 10.2.1 Exploring the Granger causality test
- 10.3 Forecasting real disposable income and real consumption
- 10.4 Next steps
- 10.5 Exercises
- 10.5.1 Use a VARMA model to predict realdpi and realcons
- 10.5.2 Use a VARMAX model to predict realdpi and realcons
- Summary
- 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia
- 11.1 Importing the required libraries and loading the data
- 11.2 Visualizing the series and its components
- 11.3 Modeling the data
- 11.3.1 Performing model selection
- 11.3.2 Conducting residual analysis
- 11.4 Forecasting and evaluating the model's performance
- Next steps
- Part 3. Large-scale forecasting with deep learning.
- 12 Introducing deep learning for time series forecasting
- 12.1 When to use deep learning for time series forecasting
- 12.2 Exploring the different types of deep learning models
- 12.3 Getting ready to apply deep learning for forecasting
- 12.3.1 Performing data exploration
- 12.3.2 Feature engineering and data splitting
- 12.4 Next steps
- 12.5 Exercise
- Summary
- 13 Data windowing and creating baselines for deep learning
- 13.1 Creating windows of data
- 13.1.1 Exploring how deep learning models are trained for time series forecasting
- 13.1.2 Implementing the DataWindow class
- 13.2 Applying baseline models
- 13.2.1 Single-step baseline model
- 13.2.2 Multi-step baseline models
- 13.2.3 Multi-output baseline model
- 13.3 Next steps
- 13.4 Exercises
- Summary
- 14 Baby steps with deep learning
- 14.1 Implementing a linear model
- 14.1.1 Implementing a single-step linear model
- 14.1.2 Implementing a multi-step linear model
- 14.1.3 Implementing a multi-output linear model
- 14.2 Implementing a deep neural network
- 14.2.1 Implementing a deep neural network as a single-step model
- 14.2.2 Implementing a deep neural network as a multi-step model
- 14.2.3 Implementing a deep neural network as a multi-output model
- 14.3 Next steps
- 14.4 Exercises
- Summary
- 15 Remembering the past with LSTM
- 15.1 Exploring the recurrent neural network (RNN)
- 15.2 Examining the LSTM architecture
- 15.2.1 The forget gate
- 15.2.2 The input gate
- 15.2.3 The output gate
- 15.3 Implementing the LSTM architecture
- 15.3.1 Implementing an LSTM as a single-step model
- 15.3.2 Implementing an LSTM as a multi-step model
- 15.3.3 Implementing an LSTM as a multi-output model
- 15.4 Next steps
- 15.5 Exercises
- Summary
- 16 Filtering a time series with CNN
- 16.1 Examining the convolutional neural network (CNN).
- 16.2 Implementing a CNN
- 16.2.1 Implementing a CNN as a single-step model
- 16.2.2 Implementing a CNN as a multi-step model
- 16.2.3 Implementing a CNN as a multi-output model
- 16.3 Next steps
- 16.4 Exercises
- Summary
- 17 Using predictions to make more predictions
- 17.1 Examining the ARLSTM architecture
- 17.2 Building an autoregressive LSTM model
- 17.3 Next steps
- 17.4 Exercises
- Summary
- 18 Capstone: Forecasting the electric power consumption of a household
- 18.1 Understanding the capstone project
- 18.1.1 Objective of this capstone project
- 18.2 Data wrangling and preprocessing
- 18.2.1 Dealing with missing data
- 18.2.2 Data conversion
- 18.2.3 Data resampling
- 18.3 Feature engineering
- 18.3.1 Removing unnecessary columns
- 18.3.2 Identifying the seasonal period
- 18.3.3 Splitting and scaling the data
- 18.4 Preparing for modeling with deep learning
- 18.4.1 Initial setup
- 18.4.2 Defining the DataWindow class
- 18.4.3 Utility function to train our models
- 18.5 Modeling with deep learning
- 18.5.1 Baseline models
- 18.5.2 Linear model
- 18.5.3 Deep neural network
- 18.5.4 Long short-term memory (LSTM) model
- 18.5.5 Convolutional neural network (CNN)
- 18.5.6 Combining a CNN with an LSTM
- 18.5.7 The autoregressive LSTM model
- 18.5.8 Selecting the best model
- 18.6 Next steps
- Part 4. Automating forecasting at scale
- 19 Automating time series forecasting with Prophet
- 19.1 Overview of the automated forecasting libraries
- 19.2 Exploring Prophet
- 19.3 Basic forecasting with Prophet
- 19.4 Exploring Prophet's advanced functionality
- 19.4.1 Visualization capabilities
- 19.4.2 Cross-validation and performance metrics
- 19.4.3 Hyperparameter tuning
- 19.5 Implementing a robust forecasting process with Prophet.
- 19.5.1 Forecasting project: Predicting the popularity of "chocolate" searches on Google.
