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

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