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Reframe Time Series data into Supervised Learning
Reframe Time Series data into Supervised Learning
Why should you split your dataset
Why should you split your dataset
Steps in DS project
Steps in DS project
2D graphs
2D graphs
Missing data is a problem
Missing data is a problem
How to address imbalanced data?
How to address imbalanced data?
Granger causality
Granger causality
Triple Exponential Smoothing
Triple Exponential Smoothing
Recurrent Neural Networks
Recurrent Neural Networks
What kind of Time Series model is Prophet?
What kind of Time Series model is Prophet?
Advantages of Prophet
Advantages of Prophet
Imbalanced data
Imbalanced data
Forecast Bias
Forecast Bias
Model doesn't behave well with real data
Model doesn't behave well with real data
When not to use Prophet?
When not to use Prophet?
Clean your time series data
Clean your time series data
Select your Time Series model
Select your Time Series model
Simple Exponential Smoothing
Simple Exponential Smoothing
MinMax Scaling
MinMax Scaling
Advantages of RNNs
Advantages of RNNs
Best scenarios for Prophet
Best scenarios for Prophet
Double Exponential Smoothing
Double Exponential Smoothing
Feature scaling
Feature scaling
Handle Missing Values
Handle Missing Values
SMOTE for imbalanced data
SMOTE for imbalanced data
Time Series models classification
Time Series models classification
Test for Granger causality
Test for Granger causality
Nature of missing data
Nature of missing data
Time bias
Time bias
Example of nature of missing data
Example of nature of missing data
Architecture of RNNs
Architecture of RNNs
Forecast Interval Coverage as a Performance Metric
Forecast Interval Coverage as a Performance Metric
ARIMA models
ARIMA models
Time Series with XGBoost
Time Series with XGBoost
Introduction to RNNs
Introduction to RNNs
Sources of Missing data
Sources of Missing data
Feature selection
Feature selection
Box-Jenkins methodology
Box-Jenkins methodology
Prediction Direction Accuracy
Prediction Direction Accuracy

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