Few tips on analyzing time-series data:
- Data Preprocessing:
- Clean and preprocess your data thoroughly. Handle missing values and outliers appropriately.
- Ensure that your time series is stationary, as many time series models assume this. If not, consider differencing the data.
- Visualize the Time Series:
- Plot your time series data to visually identify trends, seasonality, and any noticeable patterns.
- Decompose the time series into its components to better understand the underlying structure.
- Autocorrelation Analysis:
- Examine autocorrelation and partial autocorrelation functions to identify lags where the series correlates with itself.
- This analysis helps in choosing the appropriate lag order for autoregressive and moving average components in models like ARIMA.
- Model Selection and Validation:
- Choose an appropriate time series model based on the characteristics of your data. Consider ARIMA, SARIMA, or machine learning models like LSTM.
- Split your data into training and testing sets for model validation. Validate the model’s performance using metrics like MSE, RMSE, and MAE.