The Statistical Techniques of Artificial Intelligence with python For Time Series Forecasting

The Artificial Intelligence with the Time series forecasting with a critical component which enables the organization to make a structured decisions on the historic of the data. This paper explores the several techniques of statistics for the time series forecasting which includes Autoregressive Integrated Moving Average(ARIMA), Exponential Smoothing State Space Model(ETS), and Seasonal Decomposition of Time Series(STL). We can also implement these methods to evaluate the performance of the techniques with the dataset of Air passengers from 1949 to 1960. The ARIMA which captures the underlying patterns of the data, whereas ETS demonstrates the accuracy of the data with the seasonal components, and STL decomposes the technique which provides valuable things according to the trend of the time series. This study not only focuses on the strength and the weakness of the methods but also provides practical implementation with the code which will be increasing in the knowledge in time series forecasting within the field of Artificial Intelligence. The main aim of this paper to compare these techniques by analyzing the results of the study which can be applied to the Artificial Intelligence and providing which will be most suitable approach for the forecasting of time series needs.