Approaches for Predicting Time Series Data

Requirements Clarification & Assessment

1. Understanding the Context:

   • Determine the specific business problem or application for which time series forecasting is needed.
   • Identify the goals of the forecast: accuracy, interpretability, speed, etc.

2. Data Availability:

   • Assess the availability of historical data, including its length, frequency, and any missing values.
   • Determine if external data sources or covariates are available that could enhance the forecast.

3. Data Characteristics:

   • Identify any trends, seasonal patterns, or cyclical behaviors present in the data.
   • Evaluate the presence of outliers or anomalies that might affect the model.

4. Technical Constraints:

   • Consider computational resources available for model training and prediction.
   • Evaluate the expertise available in terms of statistical methods and machine learning models.

5. Performance Metrics:

   • Define the metrics to evaluate the forecast accuracy (e.g., MAE, RMSE, MAPE).
   • Determine acceptable thresholds for model performance based on business needs.

6. Time Constraints:

   • Establish the time frame for model development and deployment.
   • Identify the frequency of forecast updates needed.