Time Series Predictions With Tensorflow
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1) The document discusses time series predictions using both statistical and neural network approaches. 2) With statistical models, the time series data must be made stationary by removing trends and seasonal variations in order to find the best fitting linear regression line for predictions. 3) Neural networks can directly model trends and seasons in time series data without prior data preprocessing, resulting in potentially more accurate predictions compared to statistical models.
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Time Series Predictions With Tensorflow
- 1. 2018 IBM Systems Technical University August / 2018 São Paulo Time Series Predictions With Tensorflow Paulo Queiroz Systems Consultant IBM Systems Lab Services
- 2. Session Objectives — Concepts of time series predictions — Preparing and understanding the data – Trends / Seasons – Stationary data – Transformations — Statistical approach & Linear Models — Neural Network approach – LSTM © Copyright IBM Corporation 20182
- 3. Time series processing Different of normal data series estimations, time series might require a different approach when predicting or explaining the data movements ● If I want to predict jacket sales, does the month matter ? ● How many beers should I expect to sell in the morning ? Time Series ( TS ) introduce the concept of seasonal trends, which might make usual analisis methods less efficient © Copyright IBM Corporation 20183
- 4. If a sample TS dataset is loaded some information becomes proeminent: ● The data has a positive trend ● It‘s high seasonal Does this variations affect the prediction ? How to handle these effects ? © Copyright IBM Corporation 20184 Time series processing
- 5. Statistical models are made to analyse only stationary ( no trend no season ) data This happens mainly due the way that regressive models works, which relies on always finding the best fit straight line to the points. ● If the trend is negative the prediction will be negative as the line progression is negative ● Between the seasons the trend changes which will affect the line The key point when the data isn‘t stationary is identify the trends and work into make it stationary, by removing the noise ( it‘s a pain ) © Copyright IBM Corporation 20185 Stationary data
- 6. Make the data Stationary There are a few tricks to make the data stationary and isolate non-stationary data as noise. They mainly goes around logarithm transformation of the numbers and shift the differentiation © Copyright IBM Corporation 20186 After the differentiation, we can assume with near 90% accuracy that the residual data is stationary
- 7. Make Predictions ! Within the linear approach, when a prediction is wanted, the most complete model is called ARIMA ( Auto Regressive Integrated Moving averages ). To simplify, ARIMA models can be seen as a composition of linear regressions with tweaks. While a simple linear regression looks like y = mx+b, our ARIMA looks like: ŷt = μ + 1 yt-1 +…+ p yt-p - θ1et-1 -…- θqet-qϕ ϕ Before begin predicting… it‘s important to train ARIMA with the data, and the trained data will look like this: © Copyright IBM Corporation 20187
- 8. Re-insert Trends The key objective of ARIMA was predict the behavior within regular the seasons, however it didn‘t take into account the trend, therefore the trend must be inserted back © Copyright IBM Corporation 20188
- 9. Code so far Python comes with tons of modules that help construct these predictions… © Copyright IBM Corporation 20189 But even with all these tools the results weren‘t so good :S Would another approach produce better results ?
- 10. Neural Network Approach Another approach is use neural networks and feed as much as data as possible to it, in order to explain the behavior. When using neural network is required to remove season and trends, as the network learn this behavior. © Copyright IBM Corporation 201810
- 11. Neural Network Approach Due that, the code base is much simpler, and only feature normalization is required due the network convergency © Copyright IBM Corporation 201811
- 12. Thank you! Paulo Queiroz Systems Consultant pqueiroz@br.ibm.com +55 0800 701 4262 ibm.com Please complete the Session Evaluation! © Copyright IBM Corporation 201812