What is meant by Time series analyses?
The term "time series analysis" refers to the examination of data collected over a specified period. Time series analysis aims to identify patterns, trends, seasonal effects, and other temporal relationships in the data. This type of analysis is particularly useful for forecasting future values and monitoring processes over time. It is commonly used in fields such as economics, finance, meteorology, and production management.
Typical software functions in the area of "time series analysis":
- Trend Analysis: Identification and representation of long-term trends in the time series data.
- Seasonal Analysis: Detection and analysis of seasonal patterns or recurring effects within the data.
- Moving Averages: Calculation and display of moving averages to smooth out the data and reduce random fluctuations.
- Forecasting Models: Development and application of models to predict future values based on historical data, such as ARIMA or exponential smoothing.
- Anomaly Detection: Identification of unusual or unexpected data patterns that may indicate deviations or issues.
- Visualization: Creation of time series charts and graphs for better interpretation and communication of results.