Seasonality can make it tough to predict future trends when looking at time series data. Sometimes, it’s really hard to tell the difference between real trends and seasonal changes.
Here are some key points to understand:
Tricky Patterns: Seasonal changes can hide important trends. This can lead to wrong predictions.
Modeling Challenges: To understand these seasonal effects, scientists use complicated models. Two examples are Seasonal Decomposition of Time Series (STL) and seasonal autoregressive integrated moving average (SARIMA). These models can be difficult to set up and check for accuracy.
To handle these challenges, data scientists need to be careful when preparing the data. They also use strong tools to find and separate seasonal patterns. This helps them make better predictions, even though the data can be complicated.
Seasonality can make it tough to predict future trends when looking at time series data. Sometimes, it’s really hard to tell the difference between real trends and seasonal changes.
Here are some key points to understand:
Tricky Patterns: Seasonal changes can hide important trends. This can lead to wrong predictions.
Modeling Challenges: To understand these seasonal effects, scientists use complicated models. Two examples are Seasonal Decomposition of Time Series (STL) and seasonal autoregressive integrated moving average (SARIMA). These models can be difficult to set up and check for accuracy.
To handle these challenges, data scientists need to be careful when preparing the data. They also use strong tools to find and separate seasonal patterns. This helps them make better predictions, even though the data can be complicated.