Hi,
In todays analysis, I Compared time series models that helps you pick the best one for predicting future data. Different models work better for different kinds of data. Here’s a simple comparison:
1. ARIMA: Good for most data but not for data that changes in a pattern (like sales in different seasons). Best for regular data that goes up and down.
2. SARIMA: Like ARIMA, but better for data with seasonal patterns (like higher ice cream sales in summer). It’s a bit complicated to use.
3. Exponential Smoothing : Easy to use and good for data that has trends and patterns that repeat every year. Not great if the data changes unexpectedly.
4. VAR (Vector Autoregression): Great for when you have several types of data and want to see how they affect each other. Needs all data types to be steady and can take a lot of computer power.
5. LSTM (Long Short Term Memory): Really good for big datasets and can understand complicated patterns. Needs a lot of data and computer power to work well.
6. Prophet (by Facebook): Made for business data that’s recorded every day. It’s good at handling special days like holidays. Not as good for data that’s not daily or very messy.
In the end, the best model often comes from trying a few and seeing which one predicts the best for your specific data. By Monday we will start analyzing our dataset.