What is Time Series Forecasting?
Do you know that Asia Pacific is the largest growing deep learning market globally with a CAGR (Compound Annual Growth Rate) of over 40%? Time series TensorFlow prediction is an important concept in deep learning & ML. All the deep learning/ML models have a respective dataset that is a collection of observations. These observations often include a time component. Time series arranges the observations sequentially in time, thus adding a new dimension to the dataset, i.e., time.
One may think about what will be achieved by increasing the dimensionality of their dataset? Well, adding a time dimension to your dataset will create a detailed level of dependence among observations. The outcome is then predicted with the help of time series forecasting of prior observations.
The use of time-series data (historical data) for predicting the future is called time series forecasting. One should also ensure that the future should be completely unknown and can only be predicted based on historical data.
The time-series data is analysed to develop models that describe the data effectively. The time series is decomposed into several components for developing apt models. Once the models that best describe the historical data are developed, it is then used for forecasting. One should not confuse time series analysis with time series forecasting as the latter comes into action only when the former is completed.
Time Series Components
Before knowing about time series TensorFlow forecasting, one should be familiar with the component of a time series. A time series is decomposed into four components during time series analysis. These components help in understanding the dataset properly. The four components of a time series are as follows:
- Trends – The behaviour of a series/dataset over time is explained by trends. The increasing and decreasing behaviour of a time series is explained by trends.
- Level – Level is the base value of the time series, considering that the representation of the series is a straight line. Many experts also define level as the average value of the series.
- Seasonality – The behaviours of the series that are repeated over time are called seasonality. Some experts also term seasonality as periodic fluctuations.
- Noise – Each dataset contains some data points/outliers that the time series model cannot explain. These datasets possess unpredictable properties and cannot be mapped via time series analysis/forecasting.
One should note that a time series must have a level and some noise. However, trends and seasonality in a time series are optional.
Why TensorFlow?
While there are many frameworks available in the market for time series forecasting, deep tech professionals prefer TensorFlow out of all the available frameworks. TensorFlow is written in Python, C++, and CUDA that are among the widely used programming languages. TensorFlow is an open-source software library designed especially for machine learning and deep learning.
TensorFlow helps us in building ML models during time series analysis that will be used for forecasting. It offers users to build various types of models like CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), etc. TensorFlow can help you forecast a single time step or multiple depending upon the user and the dataset. While time series TensorFlow forecasting, you can:
- Forecast a single feature/dimension for a single timestep.
- Forecast all the dimensions of the dataset for a single timestep.
- Forecast for multiple steps in a single-shot (making all predictions at once).
- Forecast for multiple steps where one prediction is made at a time (autoregressive).
Setting up Data for Time Series Forecasting
Before time series forecasting, we need to prepare the data accordingly. The setup for time series forecasting includes various processes like data extraction, visualisation, exploration, cleaning, etc. Let us see the steps behind setting up the data for time series forecasting:
- The basic step is to import the data or extract the data. You need to have a dataset from which rich insights will be extracted using time series forecasting.
- The next step is data cleaning, where you remove the duplicate or irrelevant data points. Missing data is also handled at this step. If there any outliers discovered early, remove them from the dataset. The structural errors can also be fixed under data cleaning.
- The quantities in a time series that vary with time are termed as signals. These signals also represent various physical events in a time series. For example, if there is a time series of weather descriptions of any location over different days, then the signals would be the physical events like temperature, rainfall, etc. Proceed by converting the signals in sin cos format.
- Along with converting signals into sin cos format, convert the time & date into seconds. Once you are done, plot the time and sin cos function.
- Before forecasting and training our data, we will split the data based on time for further simplification.
- The data is now to be normalised before training. The simplest step for data normalisation is to subtract the mean of the dataset from each data point and then divide it by standard deviation.
- Once you are done with data normalisation, you have to check for data bias. Data bias is defined as a condition where some elements of the dataset are heavily represented as compared to other elements. Biased data results in lower accuracy along with skewed analytics. You can plot a violin plot to remove any data bias present in your dataset.
Data Windowing
You must be wondering that there are a lot of processes involved before time series forecasting. Well, young tech enthusiasts should know that data preparation is a crucial step before training or analysing the data. Data windowing is the last step in setting up the data for time series TensorFlow forecasting. Data windowing helps in using the data with various models without any concerns.
For example, if a model is predicting 1 hour into the future, you need a 6-hour window of historical data to predict effectively. The historical data window is termed as ‘input width’ while the data window to be predicted is termed as ‘offset’ during data windowing.
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TensorFlow Single Step Models
When you have the dataset in its simplest and predict only for one timestep in the future, you will use TensorFlow’s single-step models. For example, if you have created a data window of ‘hours’, then predicting for one timestep, meaning you are predicting one hour in the future. A ‘WindowGenerator’ object is created in TensorFlow to develop single-step pairs, i.e., input and label.
It is also suggested to create a baseline model before training your model. Creating a baseline model will help you in comparing the initial single-step model with future complicated models. For example, if you are predicting temperature for 1 hour in the future, then create a baseline model that returns the current temperature as a prediction (no change).
Since temperatures change slowly over time, it is possible to have no change in temperature after an hour. Note that the baseline model will not be effective when predicting multiple timesteps.
Another example of creating a single timestep model using TensorFlow is to insert a linear transformation between input and output. Ensure the output depends only on the single timestep that is to be evaluated while creating a linear model using TensorFlow. The dense model is another single-step model creating using TensorFlow.
Convolution neural networks via TensorFlow can also be used for predicting for a single timestep. A convolution layer in TensorFlow will take multiple time steps as input for predicting for a timestep. RNNs are also used widely for time series forecasting.
The internal state of the machine also jumps from a timestep to another because RNNs processes a time series step-by-step (by default). Time series TensorFlow forecasting via RNN is a good choice as RNN models tend to remember a long history of inputs (historical data).
TensorFlow Multi-Step Models
In multi-step models, the prediction is made over a range of timesteps. Unlike a single-step model, a multi-step model predicts a range of future values using historical data. Multi-step models can predict the entire future time series at once via single-shot prediction. You can also use autoregressive prediction, where the model makes a single step prediction at a time. The outcome of the single-step prediction in autoregressive prediction works as the input for the next time step prediction.
Conclusion
Time series TensorFlow prediction provides accurate results as compared to other frameworks. We discussed some of the single-step models (CNN, RNN, Dense, etc.) that can be used for single-step prediction via TensorFlow.
You can also predict a range of future values by using multi-step models. One should also note that data preparation is a key step before time series TensorFlow prediction. You can learn more about time series prediction in DL (Deep Learning) and ML by opting for the upGrad’s Advanced Certificate Programme in DL & ML. Start learning time series forecasting with TensorFlow!
