Technology Solutions Private Limited
Technology Solutions Private Limited
Time series analysis is a specialized method for studying a sequence of data points gathered over a time span. Unlike random recording, analysts collect data at consistent intervals over a defined period. To ensure reliability, this analysis usually necessitates a substantial number of data points. Its primary application lies in forecasting, where future data is predicted based on historical data.
Time series analysis aids organizations in comprehending the root causes of trends or consistent patterns over time. Through data visualizations, business users can observe seasonal trends and delve into the reasons behind these patterns.
Time series forecasting is an integral aspect of predictive analytics. It reveals probable alterations in the data, such as seasonality or cyclic behavior, offering an enhanced understanding of data variables and improving the accuracy of forecasts.
Data variations
1-Descriptive Analysis
Visualization Techniques
Summary Statistics
Time Series Decomposition
2-Trend Component
Seasonal Component
Cyclic Component
Irregular or Random Component
Time series analysis models and techniques offer a wide array of tools to capture, model, and forecast patterns within sequential data. From traditional statistical models to advanced machine learning techniques, selecting the appropriate model requires consideration of data characteristics, complexity, and forecasting requirements.