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

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