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

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