Time-series model

As explained in Effective Data Analysis

A time series model uses observations ordered over time to estimate or predict future values while accounting for temporal patterns.

A time-series model uses historical observations ordered over time to estimate values in future periods. It accounts for temporal patterns such as seasonality, overall trends, and recent trends. section 9.1.1

Forecasting questions can include:

• What will a city’s daily electricity consumption be over the next seven days? section 9.1.1
• What will a store’s weekly sales be, considering upcoming holidays? section 9.1.1
• How many hospitalizations for a disease might occur in the coming season? section 9.1.1

Forecasting requires accounting for seasonality, trend, and noise. ARIMA and Holt-Winters are examples of methods that can identify and adjust for trends. section 9.1.1

Figure 9.5 Time series models look for patterns in data captured over time to predict future values.

A visual overview of how patterns in observations over time support forecasts. section 9.1.1

How Effective Data Analysis covers it

How deep it goes

Identifies seasonality, trend, and noise as forecasting components, but does not detail model fitting.

Examples

Uses examples of electricity consumption, holiday-aware retail sales, hospitalizations, and revenue forecasts.

What you can do after

Apply it to forecast values for future periods, such as daily electricity use or weekly sales.

How it connects

Links forecasting to seasonality, trend, noise, ARIMA, and Holt-Winters.

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Also explained in

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  Statistics Every Programmer NeedsA time series model describes observations indexed over time and their temporal dependencies or patterns.

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Related concepts

• seasonality effectuses · A seasonality effect is a recurring pattern of variation in data associated with regular temporal, calendar, or time-of-year cycles.
• forecast baselineproduces · A reference forecast generated by a forecasting process against which demand planning decisions or improved forecasts can be compared.
• judgmental forecastcontrasts with · A judgmental forecast is a prediction of future demand that incorporates human assessment rather than relying solely on an automated forecasting model.
• demand forecastcontrasts with · A demand forecast is an estimate or prediction of the quantity of a product, service, or resource that will be needed during a specified future period.
• trenduses · A trend is a developing or persistent direction, pattern, or movement in observations, conditions, behavior, or attention.

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.