Predict with Data

Use your data and MATLAB^® add-on toolboxes to predict trends and estimate the future of your sensor data

Find trends in your data and use MATLAB add-on toolboxes to predict future measurements. Complete predictive analytics by training a neural network or completing regression analysis on your data.

Topics

Fit an Autoregression Model to the Tidal Depth Data
This example shows how to fit an autoregression (AR) model for data in your ThingSpeak™ channel and calculate the regression parameters along with their uncertainties.
Fit a Linear Regression Model
This example shows how to fit a linear regression model for data in your ThingSpeak™ channel and calculate the regression coefficients in the data.
Compute Linear Response Characteristics
This example shows how to compute the linear response characteristics of the storing tank temperature of a solar water heater.
Generate Chirp Signal
This example shows how to generate a linear chirp signal on ThingSpeak™.

Featured Examples

Create and Train a Feedforward Neural Network

Train a feedforward neural network to predict temperature.

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Forecast Tidal Depths Using ThingSpeak Data

Forecast the tidal depth at Ockway Bay using data stored in a ThingSpeak™ channel. Predicting tidal depths is vital: if you are not aware of the water depth, your boat can get easily stuck in the mud in a shallow bay.

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Visualize Linear Fit to Scattered ThingSpeak Data

Visualize the trend between ambient temperature and relative humidity using Curve Fitting Toolbox&trade.

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Visualize and Compare Measured and Predicted Tidal Depths

Compare the measured and predicted tidal depth at Ockway Bay. Predicting tidal depths is vital: if you are not aware of the water depth, your boat can get easily stuck in the mud in a shallow bay.

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