About Time Series

Time Series

Time Series data analysis is a powerful statistical technique that deals with the examination and modeling of data points collected sequentially over time. It aims to capture underlying patterns, trends, and seasonality within the data to forecast future values and make informed decisions. It is widely used in various fields, such as finance, economics, meteorology, and social sciences. Some of the primary objectives of time series data analysis include:

Descriptive analysis: The first step in time series data analysis is to explore and describe the data to isolate its key features, such as trends, seasonality, and cycles. Visualization techniques, such as line plots and decomposition plots, can help reveal these patterns and guide further analysis.

Identifying causal relationships: Time series data analysis can help determine the relationships between different variables over time. This can be useful for understanding the underlying causes of the observed behavior and for assessing the potential impact of external factors or interventions on the variable of interest.

Forecasting: One of the most important goals of time series data analysis is to make accurate and reliable forecasts about future values of the series. Various statistical models, such as ARIMA, SARIMAX, VAR, and more recently, machine learning techniques, can be used to forecast future values based on the historical data.

One of the building blocks of Time Series data analysis are ARMA models, which stand for Auto-regressive Moving Average. The AR term, also referred to as long term memory, allows to forecast a series based solely on the past values in the series - called lags. Whereas, the MA term, also known as short memory, forecasts a series based solely on the past errors in the series - called error lags. ARMA models are suitable for stationary time series data, where the mean, variance, and autocorrelation structure do not change over time. However, if the data is not stationary we can employ, ARIMA models, which stands for Auto-regressive Integrated Moving Average. ARIMA models add an integration component to ARMA models, which make the time series data stationary by subtracting the previous observation from the current observation. ARIMA models have three components: ‘p’ - the order of the autoregressive component, ‘d’ - the order of differencing, and ‘q’ - the order of the moving average component.