Time series forecasting is the task of creating a model to predict future values based on previously observed values. Time series forecasting is used in various different industries from retail for forecasting the daily sales volume to agriculture for forecasting weather to guide planning decisions around planting and harvesting. Tiyaro allows an easy way to train and quickly predict your time series using classical and newer machine learning methods.
These are the 5 simple steps to create a time series forecasting API:
Step 1: Gather your time series dataset:
Before we dive into the part of training the model, it is important to have a clean dataset in the correct format for our training time series forecasting models using Tiyaro's EasyTrain. The format required for the dataset can be found over in our Tiyaro Docs.
Step 2: Create an EasyTrain Job:
The quickest way to train a time series forecasting model is by going over the side tab and clicking on Retrain
Select a model class for our case Time Series forecasting.
Step 3: Upload your dataset and fill out training job information:
Select all the models you want to train. EasyTrain takes care of hyperparameter optimization.
Step 4: Start Training your EasyTrain Job:
Step 5: Enjoy your results.
As you can see, we have trained 7-time series forecasting models on our dataset.
Here, we show the time series forecasting Prophet model, we can use the demo tab to see the forecasted values in the future.
The trained model page showcases information about the API as well as the speed and cost of the model.
As we saw it is easy for a user to use Tiyaro EasyTrain to create time series forecasting models for their particular use case. Tiyaro makes it easier for the user to quickly train a wide variety of Time Series Forecasting models. So you can spend more time analyzing the time series and less time training mundane Time Series forecasting models for your use case.
Wish to create one? Head on over to Tiyaro!