Time series

What does time series mean?

A time series is a sequence of data points collected or recorded over successive, equally spaced time intervals. It represents the behavior or observations of a variable or phenomenon over time. In a time series, the data points are typically ordered chronologically, and the time intervals between them are uniform. It's assumed that a linear relationship exists between the variables:

yt = β0 + β1x1t + β2x2t + · · · + βkxkt + et,

where et is the residual error of the model at time t, B0 is a constant and coefficient B1 is the effect of regressor x1 after taking into account the effect of all k regressors involved in the model.

Why is time series related to demand forecasting?

Time series analysis involves studying and analyzing the patterns, trends, and characteristics present in the data to gain insights, make forecasts, and develop models for future predictions. It helps uncover underlying patterns, seasonal variations, cyclical patterns, and other important features that can aid in understanding and predicting future behavior. Let's look at some components:

• Trend identification: Time series analysis allows the identification of long-term trends in demand. By analyzing the overall direction of the data, analysts can determine whether demand is increasing, decreasing, or remaining stable over time. This trend information is crucial for forecasting future demand and adjusting business strategies accordingly.
• Seasonality detection: Time series analysis helps detect seasonal patterns by decomposing the data or using other techniques. Understanding seasonality is essential for accurate demand forecasting, as it allows businesses to anticipate and plan for periods of increased or decreased demand.
• Forecasting models: Time series analysis provides the foundation for building forecasting models. Techniques like exponential smoothing, autoregressive integrated moving average (ARIMA), or seasonal decomposition of time series are commonly used to develop forecasting models based on historical demand data. These models take into account trends, seasonality, and other relevant factors to project future demand levels.