English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 84 Lessons (11h 4m) | 1.47 GB
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.
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.
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
Table of Contents
1 Part 1. Time waits for no one
2 Chapter 1. Understanding time series forecasting
3 Chapter 1. Bird’s-eye view of time series forecasting
4 Chapter 1. How time series forecasting is different from other regression tasks
5 Chapter 2. A naive prediction of the future
6 Chapter 2. Implementing the historical mean baseline
7 Chapter 2. Forecasting last year’s mean
8 Chapter 3. Going on a random walk
9 Chapter 3. Identifying a random walk
10 Chapter 3. Testing for stationarity
11 Chapter 3. The autocorrelation function
12 Chapter 3. Forecasting a random walk
13 Chapter 3. Next steps
14 Part 2. Forecasting with statistical models
15 Chapter 4. Modeling a moving average process
16 Chapter 4. Identifying the order of a moving average process
17 Chapter 4. Forecasting a moving average process Part 1
18 Chapter 4. Forecasting a moving average process Part 2
19 Chapter 4. Next steps
20 Chapter 5. Modeling an autoregressive process
21 Chapter 5. Finding the order of a stationary autoregressive process
22 Chapter 5. Forecasting an autoregressive process
23 Chapter 6. Modeling complex time series
24 Chapter 6. Examining the autoregressive moving average process
25 Chapter 6. Identifying a stationary ARMA process
26 Chapter 6. Devising a general modeling procedure
27 Chapter 6. Selecting a model using the AIC
28 Chapter 6. Understanding residual analysis
29 Chapter 6. Applying the general modeling procedure
30 Chapter 6. Forecasting bandwidth usage
31 Chapter 6. Exercises
32 Chapter 7. Forecasting non-stationary time series
33 Chapter 7. Forecasting a non-stationary times series Part 1
34 Chapter 7. Forecasting a non-stationary times series Part 2
35 Chapter 8. Accounting for seasonality
36 Chapter 8. Forecasting the number of monthly air passengers
37 Chapter 8. Forecasting with a SARIMA(p,d,q)(P,D,Q)m model
38 Chapter 9. Adding external variables to our model
39 Chapter 9. Exploring the exogenous variables of the US macroeconomics dataset
40 Chapter 9. Forecasting the real GDP using the SARIMAX model
41 Chapter 10. Forecasting multiple time series
42 Chapter 10. Designing a modeling procedure for the VAR(p) model
43 Chapter 10. Forecasting real disposable income and real consumption
44 Chapter 10. Next steps
45 Chapter 11. Capstone Forecasting the number of antidiabetic drug prescriptions in Australia
46 Chapter 11. Performing model selection
47 Part 3. Large-scale forecasting with deep learning
48 Chapter 12. Introducing deep learning for time series forecasting
49 Chapter 12. Getting ready to apply deep learning for forecasting
50 Chapter 12. Feature engineering and data splitting
51 Chapter 13. Data windowing and creating baselines for deep learning
52 Chapter 13. Implementing the DataWindow class
53 Chapter 13. Multi-step baseline models
54 Chapter 14. Baby steps with deep learning
55 Chapter 14. Implementing a multi-output linear model
56 Chapter 14. Implementing a deep neural network as a multi-step model
57 Chapter 15. Remembering the past with LSTM
58 Chapter 15. Examining the LSTM architecture
59 Chapter 15. Implementing the LSTM architecture
60 Chapter 15. Implementing an LSTM as a multi-output model
61 Chapter 16. Filtering a time series with CNN
62 Chapter 16. Implementing a CNN
63 Chapter 16. Implementing a CNN as a multi-step model
64 Chapter 17. Using predictions to make more predictions
65 Chapter 17. Building an autoregressive LSTM model
66 Chapter 18. Capstone Forecasting the electric power consumption of a household
67 Chapter 18. Data wrangling and preprocessing
68 Chapter 18. Feature engineering
69 Chapter 18. Utility function to train our models
70 Chapter 18. Long short-term memory (LSTM) model
71 Part 4. Automating forecasting at scale
72 Chapter 19. Automating time series forecasting with Prophet
73 Chapter 19. Exploring Prophet
74 Chapter 19. Basic forecasting with Prophet
75 Chapter 19. Exploring Prophet’s advanced functionality
76 Chapter 19. Hyperparameter tuning
77 Chapter 19. Forecasting project Predicting the popularity of “chocolate” searches on Google
78 Chapter 19. Experiment Can SARIMA do better
79 Chapter 20. Capstone Forecasting the monthly average retail price of steak in Canada
80 Chapter 20. Modeling with Prophet
81 Chapter 20. Optional Develop a SARIMA model
82 Chapter 21. Going above and beyond
83 Chapter 21. Deep learning methods for forecasting
84 Chapter 21. Other applications of time series data
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