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- Data Collection and Exploration:
- Gather historical time-ordered data.
- Explore and visualize the data to understand its patterns and characteristics.
- Stationarity:
- Check for stationarity in the time series data. Stationary time series have constant statistical properties over time, making them easier to model.
- Decomposition:
- Decompose the time series into its components, such as trend, seasonality, and noise, to better understand its structure.
- Model Selection:
- Choose a suitable forecasting model based on the characteristics of the time series data. Common models include:
- ARIMA (AutoRegressive Integrated Moving Average): A popular model that combines autoregression, differencing, and moving averages.
- Exponential Smoothing (ETS): Another approach that considers error, trend, and seasonality components.
- Prophet: Developed by Facebook, it’s designed for forecasting with daily observations that display patterns on different time scales.
- Training the Model:
- Split the data into training and testing sets.
- Train the selected forecasting model using the training set.
- Validation:
- Validate the model’s performance using the testing set.
- Evaluate the model’s accuracy using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE).
- Hyperparameter Tuning:
- Adjust the model’s hyperparameters to improve its performance.
- Forecasting:
- Use the trained model to make predictions for future time points.
- Evaluation:
- Evaluate the forecasting accuracy on new, unseen data.
- Iterate:
- Refine the model and repeat the process if necessary, especially if the characteristics of the time series change over time.
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