Time Series Forecasting

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Time Series Forecasting

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What is Time Series Forecasting – Time Series Forecasting involves the use of various modelling approaches e.g. ARIMA, ETS, etc. for purposes of forecasting the behaviour of the series into the future. Modelling algorithms for Time Series Forecasting consume Time Series data as input and provide Time Series Forecasts as output. Auto-Regressive Integrated Moving Average (ARIMA) models and Exponential Smoothing (ETS) models provide two different approaches to time series forecasting. Exponential Smoothing (ETS) and ARIMA models are the two most widely-used approaches to time series forecasting, and provide complementary techniques for purposes of Time Series Forecasting. While exponential smoothing models were based on a description of trend and seasonality in the data, ARIMA models aim to describe the autocorrelations in the data.

In statistics and econometrics, an Auto-Regressive Integrated Moving Average (ARIMA) model is very commonly used technique for purposes of forecasting. These models are fitted to time series data either to better understand the data or to predict future points in the series i.e. for purposes of forecasting. ARIMA models are applied in some cases where data show evidence of non-stationarity, where an initial differencing step (corresponding to the “integrated” part of the model) can be applied to reduce the non-stationarity. The forecasting capability in VisualizeIT offers a combination of both ETS and ARIMA forecast models.

Exponential Smoothing (ETS) is a very popular approach to produce a smoothed Time Series. Whereas in Single Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observation get older. In other words, recent observations are given relatively more weight in forecasting than the older observations. In the case of moving averages, the weights assigned to the observations are the same and are equal to 1/N. In Exponential Smoothing (ETS), however, there are one or more smoothing parameters to be determined (or estimated) and these choices determine the weights assigned to the observations.

Non-seasonal Auto-Regressive Integrated Moving Average (ARIMA) models are generally denoted ARIMA(p, d, q) where parameters p, d, and q are non-negative integers, p is the order of the Autoregressive model, d is the degree of differencing, and q is the order of the Moving-average model. Seasonal ARIMA models are usually denoted ARIMA(p, d, q)(P, D, Q)_m, where m refers to the number of periods in each season, and the uppercase P, D, Q refer to the autoregressive, differencing, and moving average terms for the seasonal part of the ARIMA model. ARIMA models form an important part of the Box-Jenkins approach to time-series modelling.

When two out of the three terms are zeros, the model may be referred to be based on the non-zero parameter, dropping “AR”, “I” or “MA” from the acronym describing the model. For example, ARIMA (1,0,0) is AR(1), ARIMA(0,1,0) is I(1), and ARIMA(0,0,1) is MA(1).

Keen to Learn More – If you are keen to learn more about Time Series Forecasting we would recommend the following for reading –

  • Otexts – https://www.otexts.org/fpp
  • Analytics Vidhya – http://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/

Modelling Solution: VisualizeIT offers access to a bunch of Analytical Models, Statistical Models and Simulation Mcropped-visualize_it_logo__transparent_090415.pngodels for purposes of Visualization, Modelling & Forecasting. Access to all the Analytical (Mathematical) models is free. We recommend you try out the Analytical models at VisualizeIT which are free to use and drop us a note with your suggestions, input and comments. You can access the VisualizeIT website here and the VisualizeIT modelling solution here –VisualizeIT.

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