A time-series database includes sequences of values or events accessed over the repeated assessment of time. The values are generally calculated at equal time intervals (e.g., hourly, daily, weekly). Time-series databases are popular in many applications, such as stock market analysis, economic and sales forecasting, budgetary analysis, utility studies, inventory studies, yield projections, workload projections, process and quality control, observation of natural phenomena (including atmosphere, temperature, wind, and earthquake), numerical and engineering experiments, and medical treatments.
A time-series database is also a sequence database. A sequence database is any database that includes sequences of ordered events, with or without a concrete approach of time. For example, Web page traversal sequences and customer shopping transaction sequences are sequence data, but they may not be time-series data.
With the growing deployment of a large number of sensors, telemetry devices, and other online data collection tools, the amount of time-series data is increasing rapidly, often in the order of gigabytes per day (such as in-stock trading) or even per minute (such as from NASA space programs).
A time series involving a variable Y, representing, say, the daily closing price of a share in a stock market, can be viewed as a function of time t, that is, Y = F(t). Trend analysis includes the following four major elements or movements for featuring time-series data −
Trend or long-term movements − These indicate the general direction in which a time series graph is moving over a long interval of time. This movement is displayed by a trend curve or a trend line. For example, the trend curve is indicated by a dashed curve. Typical methods for determining a trend curve or trend line include the weighted moving average method and the least-squares method, discussed later.
Cyclic movements or cyclic variations − These refer to the cycles, that is, the long-term oscillations about a trend line or curve, which may or may not be periodic. That is, the cycles need not necessarily follow exactly similar patterns after equal intervals of time.
Seasonal movements or seasonal variations − These are systematic or calendar-related. Examples include events that recur annually, such as the sudden increase in sales of chocolates and flowers before Valentine’s Day or of department store items before Christmas. The observed increase in water consumption in summer due to warm weather is another example. In these examples, seasonal movements are the identical or nearly identical patterns that a time series appears to follow during corresponding months of successive years.
Irregular or random movements − These characterize the sporadic motion of time series due to random or chance events, such as labor disputes, floods, or announced personnel changes within companies.