Time Series

Time Series: An Overview

• A time series is a sequence of numerical data points in successive order.
• It involves measurements that are collected at different points in time.
• This type of analysis helps to understand the underlying patterns and trends in the data.
• Examples of time series include monthly rainfall, daily temperature, or stock market prices over a period of time.

Components of a Time Series

• Time series are made up of four components: trend component, seasonal component, cyclical component, and random (or irregular) component.
• The trend component is the overall pattern or direction that the data is taking over time.
• The seasonal component captures patterns that repeat at regular intervals, such as every quarter, every month, or every week.
• The cyclical component deals with fluctuations in the data that are not of a fixed period.
• The random component, also called the irregular or residual component, captures any unexplained variation in the data after the other components have been accounted for.

Using and Interpreting Time Series Graphs

• A key tool for analysing time series is a time series graph or plot.
• This graph displays successive data points over time, represented by a line or dots connected with lines.
• Key aspects to look at in a time series graph include: the direction of the trend (increasing, decreasing, or steady); noticeable periods of seasonality; any noticeable regular patterns or cycles; and any random fluctuations or outliers.

Time series Forecasting

• One of the main reasons for studying time series is to make forecasts, predictions about future values of the series.
• This is important in various fields like finance, economics, social sciences and physical sciences.

Frequently Occurring Errors to Avoid

• Be careful not to confuse the concept of ‘correlation’ with trends in a time series plot.
• Remember that seasonal patterns are regular and predictable, while cyclic patterns are not necessarily predictable.
• Ensure your time series data is in the correct order before beginning analysis.
• It’s also important not to ignore the random component of time series in your analysis – these random changes or fluctuations can sometimes contain valuable information.