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:
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 .
The quickest way to train a time series forecasting model is by going over the side tab and clicking on Retrain
As you can see, we have trained 7-time series forecasting models on our dataset.
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 !