Use here 30, It means that the past 30 It's worth ( All features including target Columns ) To predict the 31 Target value. N_past Is the number of steps we will look at in the past when predicting the next target value.
KERAS DATA GENERATOR CODE
Let's see what's done in the code above : TestX,testY=createXY(df_for_testing_scaled,30) TrainX,trainY=createXY(df_for_training_scaled,30)
![keras data generator keras data generator](https://miro.medium.com/max/1104/1*1R6Cau0QQFbgm-iA4IkKEA.png)
Split data into X and Y, This is the most important part, Read each step correctly. ( You can also use StandardScaler) scaler = MinMaxScaler(feature_range=(0,1))ĭf_for_training_scaled = scaler.fit_transform(df_for_training)ĭf_for_testing_scaled=ansform(df_for_testing) You can notice that the data range is very large, And they are not scaled in the same range, So in order to avoid prediction errors, Let's start with MinMaxScaler Scaling data. We can't mess up the data here, Because it must be sequential in the time series. Let's look at the shape of the data : df.shape Here we try to predict “Open” The future value of the column, therefore “Open” It's the target column here. or “D”, Because we don't use dates, I just keep it as it is. Now let's take a moment to look at the data :csv The file contains Google from To Stock data for, The data is based on the frequency of days. Load data, And check the output : df=pd.read_csv("train.csv",parse_dates=,index_col=) Let's first import the libraries needed for forecasting : import numpy as npįrom import Sequentialįrom import Dense, Dropoutįrom sklearn.preprocessing import MinMaxScalerįrom _learn import KerasRegressorįrom sklearn.model_selection import GridSearchCV
KERAS DATA GENERATOR SERIES
Now we discuss time series prediction and LSTM Theoretical part. Therefore, in order to avoid the problem of long-term dependence lstm. RNN The problem is, Because the gradient disappears, They cannot remember long-term dependencies. RNN It works the same way, They remember past information and use it to process current input. So when anything happens in the movie, You already know what happened before, And it's understandable that new things happen because of what happened in the past. LSTM It's basically a cyclic neural network, Ability to handle long-term dependencies.
![keras data generator keras data generator](https://miro.medium.com/max/404/1*cmN15uFVtQpcHYklHVKIiw.png)
So just provide some simple descriptions, If you are right about LSTM I don't know much about, You can refer to our previous articles. We do not intend to discuss in detail LSTM. One thing to keep in mind when performing multivariate time series analysis, We need to use multiple features to predict the current goal, Let's take an example to understand :ĭuring the training, If we use 5 Column To train the model, We need to provide. therefore, To predict the coming count value, We must consider all columns including the target column to predict the target value. In the data above ,count Not only depends on its previous value, It also depends on other characteristics. Īs you can see in the picture, In multivariate variables, there will be multiple columns to predict the target value. īut in the case of multivariate time series data, There will be different types of eigenvalues and the target data will depend on these characteristics. Īs we can see, There is only one column, Therefore, the upcoming future value will only depend on its previous value. In the case of the real world, We mainly have two types of time series analysis :įor univariate time series data, We will use a single column to predict. Future data will depend on its previous value.
![keras data generator keras data generator](https://res.cloudinary.com/practicaldev/image/fetch/s--Sv1un7so--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/i/hy9nezuvacl390hqhsfl.png)
It can be seconds 、 minute 、 Hours 、 God 、 Zhou 、 month 、 year. Time series analysis : Time series represent a series of data based on time sequence. Today I'll share with you a wave of usage LSTM Complete code and detailed explanation for end-to-end time series prediction. We often encounter some scenes that need to be predicted, For example, predict brand sales, Forecast product sales.