**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

**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

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