Python for Time Series Data Analysis

Python for Time Series Data Analysis
Python for Time Series Data Analysis
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 15 Hours | 1.60 GB

Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!

Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!

This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.

We’ll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we’ll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.

Then we’ll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.

Afterwards we’ll get to the heart of the course, covering general forecasting models. We’ll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.

Afterwards we’ll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.

This course even covers Facebook’s Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.

So what are you waiting for! Learn how to work with your time series data and forecast the future!

We’ll see you inside the course!

What you’ll learn

  • Pandas for Data Manipulation
  • NumPy and Python for Numerical Processing
  • Pandas for Data Visualization
  • How to Work with Time Series Data with Pandas
  • Use Statsmodels to Analyze Time Series Data
  • Use Facebook’s Prophet Library for forecasting
  • Understand advanced ARIMA models for Forecasting
Table of Contents

Introduction
1 Course Overview – PLEASE DO NOT SKIP THIS LECTURE
2 Course Curriculum Overview
3 FAQ – Frequently Asked Questions

Course Set Up and Install
4 Installing Anaconda Python Distribution and Jupyter

NumPy
5 NumPy Section Overview
6 NumPy Arrays – Part One
7 NumPy Arrays – Part Two
8 NumPy Indexing and Selection
9 NumPy Operations
10 NumPy Exercises
11 NumPy Exercise Solutions

Pandas Overview
12 Introduction to Pandas
13 Series
14 DataFrames – Part One
15 DataFrames – Part Two
16 Missing Data with Pandas
17 Group By Operations
18 Common Operations
19 Data Input and Output
20 Pandas Exercises
21 Pandas Exercises Solutions

Data Visualization with Pandas
22 Overview of Capabilities of Data Visualization with Pandas
23 Visualizing Data with Pandas
24 Customizing Plots created with Pandas
25 Pandas Data Visualization Exercise
26 Pandas Data Visualization Exercise Solutions

Time Series with Pandas
27 Overview of Time Series with Pandas
28 DateTime Index
29 DateTime Index Part Two
30 Time Resampling
31 Time Shifting
32 Rolling and Expanding
33 Visualizing Time Series Data
34 Visualizing Time Series Data – Part Two
35 Time Series Exercises – Set One
36 Time Series Exercises – Set One – Solutions
37 Time Series with Pandas Project Exercise – Set Two
38 Time Series with Pandas Project Exercise – Set Two – Solutions

Time Series Analysis with Statsmodels
39 Introduction to Time Series Analysis with Statsmodels
40 Introduction to Statsmodels Library
41 ETS Decomposition
42 EWMA – Theory
43 EWMA – Exponentially Weighted Moving Average
44 Holt – Winters Methods Theory
45 Holt – Winters Methods Code Along – Part One
46 Holt – Winters Methods Code Along – Part Two
47 Statsmodels Time Series Exercises
48 Statsmodels Time Series Exercise Solutions

General Forecasting Models
49 Introduction to General Forecasting Section
50 Introduction to Forecasting Models Part One
51 Evaluating Forecast Predictions
52 Introduction to Forecasting Models Part Two
53 ACF and PACF Theory
54 ACF and PACF Code Along
55 ARIMA Overview
56 Autoregression – AR – Overview
57 Autoregression – AR with Statsmodels
58 Descriptive Statistics and Tests – Part One
59 Descriptive Statistics and Tests – Part Two
60 Descriptive Statistics and Tests – Part Three
61 ARIMA Theory Overview
62 Choosing ARIMA Orders – Part One
63 Choosing ARIMA Orders – Part Two
64 ARMA and ARIMA – AutoRegressive Integrated Moving Average – Part One
65 ARMA and ARIMA – AutoRegressive Integrated Moving Average – Part Two
66 SARIMA – Seasonal Autoregressive Integrated Moving Average
67 SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART ONE
68 SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART TWO
69 SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART 3
70 Vector AutoRegression – VAR
71 VAR – Code Along
72 VAR – Code Along – Part Two
73 Vector AutoRegression Moving Average – VARMA
74 Vector AutoRegression Moving Average – VARMA – Code Along
75 Forecasting Exercises
76 Forecasting Exercises – Solutions

Deep Learning for Time Series Forecasting
77 Introduction to Deep Learning Section
78 Perceptron Model
79 Introduction to Neural Networks
80 Keras Basics
81 Recurrent Neural Network Overview
82 LSTMS and GRU
83 Keras and RNN Project – Part One
84 Keras and RNN Project – Part Two
85 Keras and RNN Project – Part Three
86 Keras and RNN Exercise
87 Keras and RNN Exercise Solutions

Facebook’s Prophet Library
88 Overview of Facebook’s Prophet Library
89 Facebook’s Prophet Library
90 Facebook Prophet Evaluation
91 Facebook Prophet Trend
92 Facebook Prophet Seasonality