Python for Time Series Data Analysis

سرفصل های دوره

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

1 - Introduction

1 - Course Overview Check.html

1 - Course Overview PLEASE DO NOT SKIP THIS LECTURE

1 - UDEMY-TSA-FINAL.zip

2 - Course Curriculum Overview

3 - FAQ Frequently Asked Questions.html

3 - UDEMY-TSA-FINAL.zip

2 - Course Set Up and Install

4 - Installing Anaconda Python Distribution and Jupyter

4 - UDEMY-TSA-FINAL.zip

4 - the yml file.zip

3 - 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

4 - 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

5 - 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

6 - 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

7 - 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

8 - 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

9 - Deep Learning for Time Series Forecasting

2 - Quick Check on MultiVariate Time Series Notebook and Data.html

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

88 - BONUS Multivariate Time Series with RNN.html

88 - MultiVariate-RNN-with-TensorFlow-and-Keras-master.zip

89 - BONUS Multivariate Time Series with RNN

10 - Facebooks Prophet Library

90 - Overview of Facebooks Prophet Library

91 - Facebooks Prophet Library

92 - Facebook Prophet Evaluation

93 - Facebook Prophet Trend

94 - Facebook Prophet Seasonality

11 - BONUS SECTION THANK YOU

95 - BONUS LECTURE.html

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تولید کننده: Udemy-Training