Time Series Analysis
Time Series is an ordered sequence of values of a variable at equally spaced time intervals. The analysis of time series data using different statistical techniques is called as time series analysis. There are primarily two uses of time series analysis:
- To obtain an understanding of the underlying forces and structure that produced the observed data.
- Fit a model and proceed to forecasting, monitoring or even feed-back and feed-forward control.
These two uses of time series analysis require that the pattern of observed time series data is identified and at least approximately described. After the pattern is established, we can interpret and integrate it with other data, i.e. use it in our theory of the investigated phenomenon. Regardless of the depth of our understanding and the validity of our interpretation (theory) of the phenomenon, we can extrapolate the identified pattern to predict future events.
In time series analysis it is assumed that the data consist of a systematic pattern and random noise. The presence of random noise usually makes the pattern difficult to identify. Thus, most time series analysis techniques involve some form of filtering out noise in order to make the pattern more salient. Majority of the time series patterns can be described in terms of two basic classes of components: trend and seasonality. Trend represents a general systematic linear or (most often) nonlinear component that changes over time and does not repeat or at least does not repeat within the time range captured by our data. On the other hand, seasonality may have a formally similar nature, however, it repeats itself in systematic intervals over time. Those two general classes of time series components may coexist in real-life data. For example, sales of a company can rapidly grow over years but they still follow consistent seasonal patterns. Some of the common applications of Time Series Analysis are as follows:
- Sales Forecasting
- Stock Market Analysis
- Inventory Studies
- Economic Forecasting