Time series models are a category of statistical and machine learning models that are used to analyze and make predictions based on data that is collected or recorded over time. As the name suggests, it involves working on time (years, days, hours, minutes) based data, to derive hidden insights to make informed decision making. It has a natural temporal ordering, making it unique compared to cross-sectional data. Characteristics include trend, seasonality, and autocorrelation. The models are widely employed in various domains, including finance, economics, climate science, and more.
What is a Time Series?
A time series is a sequence of data points collected at regular time intervals. Time series assist in providing insights and predictions to inform decision-making. Examples of time series includes stationary series, random walks.
Components of Time Series
Time series data consists of the following components:
Trend:
This is the general tendency of data to grow or decline over a long period of time that is the long-term or downward movement in data.
Seasonality:
Seasonality is characterized by repetitive patterns or cycles at fixed intervals. It occurs due to rhythmic forces which occur in a regular & periodic manner.
Cyclical Variations:
These are movements in a time series that are not attributed to a regular movement. There is no fixed interval, uncertainty in movement and its pattern.
Irregular Variations:
These are unexpected situations/events/scenarios and spikes in a short time span.
Types of Time Series Models
Autoregressive (AR) model. AR models use the previous values of the time series to predict the current value. For example, an AR model for daily stock prices might use the closing prices from the previous day, the previous week, and the previous month to predict the closing price for today.
Moving average (MA) model. MA models use the previous errors to predict the current value. For example, an MA model for daily stock prices might use the errors from the previous day, the previous week, and the previous month to predict the error for today.
Autoregressive integrated moving average (ARIMA) model. ARIMA models are used to model non-stationary time series data, which is data that has a trend or seasonality.
Seasonal autoregressive integrated moving average (SARIMA) model. SARIMA models are like ARIMA models, but they also account for seasonality in the data.
Conclusion
Time series models are powerful tools for analyzing and forecasting time-ordered data. Selecting the right model and understanding the components of the data, are critical for accurate predictions. With the appropriate model and evaluation techniques, you can make informed decisions based on historical data trends and patterns.
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