What Is Time Series Forecasting?
Time Series represents the variation of an entity with respect to time. For example, you’ve probably heard of weather forecasting where we try to predict what the weather conditions in the future would be, on a specific time or date, or even a specific hour of the day. Other examples of time series would be the daily closing price of a company’s stock, annual sales figures, and so on.
To build an ensemble model for time series forecasting using TensorFlow, we can combine the forecasts of several different models to improve accuracy and reduce the risk of relying on a single model. Here are the steps we can follow:
- Collect and Preprocess the Data: We will need a dataset of time series data to train and validate our models. This could be any time series data, such as stock prices, weather data, or website traffic. Then we need to preprocess the data by dividing it into training, validation, and testing sets, and scaling it appropriately.
- Define a Base Model: Here we start by defining a base model that can be used as a starting point for the ensemble. This could be a simple model, such as an Auto Regressive (AR) model or a Moving Average (MA) model. We then train this base model on the training set and evaluate its performance on the validation set.
- Build Additional Models: We can then build additional models using different techniques, such as the Deep Neural Network (DNN) or the Long Short-Term Memory (LSTM) model. Each of these models would be trained on the same training set and evaluated on the validation set.
- Combine the Models: Once we have trained and evaluated our individual models, we can combine their forecasts using an ensemble method, such as Averaging or Weighting. We may also choose to use a more sophisticated ensemble method, such as stacking or boosting.
- Evaluate the Ensemble: Finally, we will evaluate the performance of our ensemble model on the testing set. We can compare the accuracy of our ensemble model with those of the individual models and the base model.
Here are some tips for building an effective ensemble model:
- Use a Diverse Set of Models: To get the most benefit from an ensemble, we need to use a diverse set of models with different strengths and weaknesses. This will help reduce the risk of relying on a single model.
- Tune the Weights Carefully: If we are using a weighted ensemble method, we should be careful to tune the weights carefully. We may need to use techniques such as cross-validation to find the optimal weights.
- Avoid Overfitting: When building our individual models, we should be careful to avoid overfitting the training data. We can use techniques such as early stopping or regularization to help prevent overfitting.
- Monitor Performance: We should continually monitor the performance of our ensemble model and individual models, and re-train them as necessary. Time series data can be subject to changes in trends or seasonality, so our models may need to be updated over time.
Overall, building an ensemble model for time series forecasting using TensorFlow can be a powerful technique for improving accuracy and reducing the risk of relying on a single model. With careful tuning and monitoring, an ensemble model can provide more accurate forecasts than any individual model.
Datasets Used for Time Series with TensorFlow for Forecasting
TensorFlow offers several built-in datasets for time series forecasting that can be used for training and evaluating models. Some of these datasets are:
- Air Passengers Dataset: This dataset contains the number of passengers on international airline flights, monthly, from 1949 to 1960.
Github source code: https://github.com/Manishms18/Air-Passengers-Time-Series-Analysis
- Daily Minimum Temperatures Dataset: This dataset contains the daily minimum temperatures (in degrees Celsius) in Melbourne, Australia, from 1981 to 1990.
Github source code: https://github.com/jbrownlee/Datasets/blob/master/daily-min-temperatures.csv
- Sunspot Dataset: This dataset contains the number of sunspots observed on the sun each year, from 1700 to 2008.
Github source code: https://github.com/jbrownlee/Datasets/blob/master/monthly-sunspots.csv
- Electricity Dataset: This dataset contains the hourly electricity consumption of a single household over a period of almost 4 years.
Github source code: https://github.com/amirrezaeian/Individual-household-electric-power-consumption-Data-Set-
- Retail Sales Dataset: This dataset contains the monthly retail sales of various products in the United States, from 1992 to 2018.
Github source code: https://github.com/topics/retail-data
These datasets can be easily loaded using the tf.keras.datasets module and used for training and evaluating time series forecasting models in TensorFlow using the code mentioned in the link below:
Source code: https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/time_series.ipynb
How Can You Deploy TensorFlow on E2E Cloud?
By using the E2E Cloud Myaccount portal -
- First login into the myaccount portal of E2E Networks with your respective credentials.
- Now, navigate to the GPU Wizard from your dashboard.
- Under the “Compute” menu on the extreme left, click on “GPU”.
- Then click on “GPU Cloud Wizard”.
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- For NGC Container TensorFlow, Click on “Next” under the “Actions” column.
- Choose the card according to requirements - A100 80GB is recommended.
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Now, choose your plan from the given options.
- Optionally you can add SSH key (recommended) or subscribe to the CDP backup.
- Click on “Create My Node”.
- Wait for a few minutes and confirm that the node is in running condition.
- Now, open the terminal on your local PC and type the following command:
ssh -NL localhost:1234:localhost:8888 root@<your_node_ip>
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- Usually the command will not show any output, which means the command has run without any error.
- Go to a web browser on your local PC and hit the url:http://localhost:1234/
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- Congratulations! Now you can run your python code inside this jupyter notebook, which has Pytorch and all the libraries frequently used in Machine Learning preconfigured.
- To get the most out of GPU acceleration, use RAPIDS and DALI which are already installed inside this container.
- RAPIDS and DALI accelerate the tasks in Machine Learning apart from learnings like data loading and preprocessing.
E2E Networks is the leading accelerated Cloud Computing player which provides the latest Cloud GPUs at great value. Connect with us at sales@e2enetworks.com
Request a free trial here: https://zfrmz.com/LK5ufirMPLiJBmVlSRml
