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

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