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Practical Time Series Data Analysis Masterclass With Statistics and Machine Learning In R
INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
Course Information (1:30)
Data and Scripts For the Course
Install R and RStudio (6:36)
Read in CSV & Excel Data (9:56)
Remove Missing Values (17:12)
More Data Cleaning (8:05)
Exploratory Data Analysis (18:53)
Start With Time Series Data
Works With Dates in R (7:33)
Pre-Processing Data With Times (8:28)
Visualize Temporal Data in R (12:35)
Components of Time Series Data (9:03)
Moving Averages (MA) For Visualizing a Trend/Pattern (4:06)
Detecting Significant Trend (5:29)
Other Ways Of Identifying Trend in Time Series Data (5:37)
Visualize Monthly Temporal Data (7:46)
Identify Cyclical Behavior with Fourier Transforms (4:21)
STL Decomposition (3:49)
Work With Seasonality (4:04)
Important Pre-Conditions of Time Series Modelling
Is My Time Series Stationary? (4:56)
Differencing: Make A Non-Stationary Time Series Stationary (8:21)
Use Mean & Variance (2:56)
Seasonal Differencing (4:46)
Detrending Time Series With Linear Regression (3:54)
Detrending Time Series With Mean Subtraction (2:28)
Time Series Based Forecasting
Simple Exponential Smoothing for Short Term Forecasts (6:33)
Other Basic Forecasting Techniques (5:04)
New Lecture
Moving Averages (MA) For Forecasting (2:50)
Simple Moving Average (4:55)
Theta Lines (5:22)
Forecasting On the Fly (7:23)
Linear Regression For Predicting Values As a Function of Time (7:38)
Linear Regression For Forecasting With Trend & Seasonality (9:13)
Lags (3:20)
Weekly Lag (2:38)
Lagged Regression (3:46)
Automatic ARIMA Model Fitting and Forecasting (3:37)
Automatic ARIMA With Real Life Data (4:40)
ARIMA With Fourier Terms (7:47)
BATS For Forecasting (6:47)
Machine Learning Techniques For Time Series Data
Linear Regression With "timetk" (6:03)
Linear Regression On Real Data (8:58)
Machine Learning Regression Models for Non-Parametric Data For Forecasting (7:07)
XGBoost For Time Series Forecasting (4:30)
Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks) (9:17)
Neural Network for Forecasting (4:06)
RNNs With Temporal Data (7:42)
Evaluate the Performance of an RNN Model (7:30)
Detecting Sudden Changes/Major Events
Detect An Anomaly in Time Series Data (8:56)
Breaks For Additive Season and Trend (BFAST) For Time Series in R (7:25)
Structural Change Detection (6:25)
Structural Changes in Forex Regime (4:57)
Simple Exponential Smoothing for Short Term Forecasts
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