Build an Autoregressive and Moving Average Time Series Model

Gained insights into time series analysis with IoT sensor data.

Compare MA and AR models to predict sensor readings accurately.

Ensured data stationarity using the Augmented Dickey-Fuller (ADF) test.

Selected the best model based on RMSE for reliable predictions.

Visualized trends and seasonality using rolling averages and autocorrelation plots.

Enabled predictive maintenance for IoT devices in industrial settings.

Improved anomaly detection for enhanced security and efficiency.

Provided energy usage forecasts for smart homes and buildings.

Offered data-driven decision-making for resource management.

Applied the methodology to industries like healthcare, manufacturing, and agriculture for optimization.