In todays class we discussed about the time series data and forecasting. The time series data refers to a sequence of data points collected or recorded at regular time intervals. This kind of data is prevalent in various fields such as economics, finance, environmental science, and more. The key characteristics of time series data include trends, seasonality, and cyclic patterns.
Sequential Nature: The primary characteristic of time series data is its sequential order. Unlike other types of data, the order in which the data points are arranged is crucial because it reflects the progression of time.
Components of Time Series:
- Trend: It represents the long-term progression of the series. Trends can be upward, downward, or even sideways over time.
- Seasonality: These are patterns that repeat at regular intervals, such as hourly, daily, weekly, monthly, or annually. For example, increased ice cream sales during summer months.
- Cyclic Patterns: Unlike seasonal patterns, cyclic patterns occur over longer periods and are not fixed to a particular time frame. They are often related to business or economic cycles.
- Irregular or Random Components: These are unforeseeable variations that are not part of the trend, seasonality, or cyclic components. They often result from unforeseen or random events.