Nat  Kutch

Nat Kutch

1597588560

Forecasting Air Passenger Numbers with TensorFlow Probability

TensorFlow Probability uses structural time series models to conduct time series forecasting. In particular, this library allows for a “scenario analysis” form of modelling — whereby various forecasts regarding the future are made.

Structural time series modelling takes the inherent characteristics of the time series into account when making forecasts. This includes factors such as the local linear trendseasonalresidual and autoregressive components. The greater the variation surrounding these components — the more uncertain the forecast.

The examples illustrated in this article use the template from the Structural Time Series modeling in TensorFlow Probability tutorial, of which the original authors (Copyright 2019 The TensorFlow Authors) have made available under the Apache 2.0 license.

United Airlines Passenger Data

For this example, a structural time series model is built in TensorFlow Probability to forecast air passenger data. The data is sourced from San Francisco Open Data: Air Traffic Passenger Statistics.

In particular, passenger numbers for United Airlines from February 2014 — June 2020 are analysed. The specific segment of passengers analysed are enplaned, domestic, departing from Terminal 3 at Boarding Area E.

Here is a visual overview of the time series:

Image for post

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Forecasting Air Passenger Numbers with TensorFlow Probability
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Nat  Kutch

Nat Kutch

1597588560

Forecasting Air Passenger Numbers with TensorFlow Probability

TensorFlow Probability uses structural time series models to conduct time series forecasting. In particular, this library allows for a “scenario analysis” form of modelling — whereby various forecasts regarding the future are made.

Structural time series modelling takes the inherent characteristics of the time series into account when making forecasts. This includes factors such as the local linear trendseasonalresidual and autoregressive components. The greater the variation surrounding these components — the more uncertain the forecast.

The examples illustrated in this article use the template from the Structural Time Series modeling in TensorFlow Probability tutorial, of which the original authors (Copyright 2019 The TensorFlow Authors) have made available under the Apache 2.0 license.

United Airlines Passenger Data

For this example, a structural time series model is built in TensorFlow Probability to forecast air passenger data. The data is sourced from San Francisco Open Data: Air Traffic Passenger Statistics.

In particular, passenger numbers for United Airlines from February 2014 — June 2020 are analysed. The specific segment of passengers analysed are enplaned, domestic, departing from Terminal 3 at Boarding Area E.

Here is a visual overview of the time series:

Image for post

#tensorflow #time-series-forecasting #data-science #time-series-analysis #machine-learning #data analysis

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