Time Series Forecasting
Understanding how data changes over time allows analysts to move beyond reporting into prediction. Time series forecasting supports planning, resource allocation, and decision-making by identifying patterns in historical data and estimating how these are likely to evolve in the future.
Technique Overview
Time Series Forecasting Definition
Time series forecasting is the process of using historical data recorded over time to estimate future values. It accounts for temporal dependency and internal structure, including trend, seasonality, and random variation. Forecasts are typically expressed as ranges rather than precise values, reflecting uncertainty. Methods range from simple moving averages to more advanced approaches such as exponential smoothing and ARIMA, selected based on the characteristics of the data and the forecasting objective (Hyndman and Athanasopoulos, 2018; Box et al., 2015).
Time Series Forecasting Description *
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Business Evidence
Strengths, weaknesses and examples of Time Series Forecasting *
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Business Application
Implementation, success factors and measures of Time Series Forecasting *
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Professional Tools
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Further Reading
Time Series Forecasting web and print resources *
Time Series Forecasting references (4 of up to 20) *
- Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M., 2015. Time series analysis: forecasting and control. John Wiley & Sons.
- Chatfield, C., 2004. The analysis of time series: an introduction. 6th ed. Boca Raton: Chapman and Hall/CRC
- Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A. and Hyndman, R.J. (2016). Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. International Journal of Forecasting, 32(3), pp.896–913. doi:https://doi.org/10.1016/j.ijforecast.2016.02.001.
- Hyndman, R.J. and Athanasopoulos, G., 2018. Forecasting: principles and practice. OTexts.
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