Time Series Analysis is a statistical technique that deals with time-ordered data. It involves understanding the underlying patterns, trends, and seasonality in data collected over time. This analysis is crucial for forecasting future values, identifying anomalies, and making informed decisions based on historical trends.
By using models like ARIMA, Exponential Smoothing, and Prophet, we can predict future data points, which is valuable in fields such as finance, economics, weather forecasting, and more.
| Date | Value |
|---|---|
| 2020-01-01 | 100 |
| 2020-02-01 | 110 |
| 2020-03-01 | 120 |
| 2020-04-01 | 200 |
| 2020-05-01 | 210 |
| 2020-06-01 | 140 |
Ensure your data includes a date column and at least one value column to perform the analysis.