1. Air Pollution Forecasting
In this tutorial, we are going to use the Air Quality dataset.
This is a dataset that reports on the weather and the level of pollution each hour for five years at the US embassy in Beijing, China.
The data includes the date-time, the pollution called PM2.5 concentration, and the weather information including dew point, temperature, pressure, wind direction, wind speed and the cumulative number of hours of snow and rain. The complete feature list in the raw data is as follows:
- No: row number
- year: year of data in this row
- month: month of data in this row
- day: day of data in this row
- hour: hour of data in this row
- pm2.5: PM2.5 concentration
- DEWP: Dew Point
- TEMP: Temperature
- PRES: Pressure
- cbwd: Combined wind direction
- Iws: Cumulated wind speed
- Is: Cumulated hours of snow
- Ir: Cumulated hours of rain
We can use this data and frame a forecasting problem where, given the weather conditions and pollution for prior hours, we forecast the pollution at the next hour.
This dataset can be used to frame other forecasting problems.
Do you have good ideas? Let me know in the comments below.
You can download the dataset from the UCI Machine Learning Repository.
Download the dataset and place it in your current working directory with the filename “raw.csv“.
2. Basic Data Preparation
The data is not ready to use. We must prepare it first.
Below are the first few rows of the raw dataset.
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No,year,month,day,hour,pm2.5,DEWP,TEMP,PRES,cbwd,Iws,Is,Ir 1,2010,1,1,0,NA,-21,-11,1021,NW,1.79,0,0 2,2010,1,1,1,NA,-21,-12,1020,NW,4.92,0,0 3,2010,1,1,2,NA,-21,-11,1019,NW,6.71,0,0 4,2010,1,1,3,NA,-21,-14,1019,NW,9.84,0,0 5,2010,1,1,4,NA,-20,-12,1018,NW,12.97,0,0 |
The first step is to consolidate the date-time information into a single date-time so that we can use it as an index in Pandas.
A quick check reveals NA values for pm2.5 for the first 24 hours. We will, therefore, need to remove the first row of data. There are also a few scattered “NA” values later in the dataset; we can mark them with 0 values for now.
The script below loads the raw dataset and parses the date-time information as the Pandas DataFrame index. The “No” column is dropped and then clearer names are specified for each column. Finally, the NA values are replaced with “0” values and the first 24 hours are removed.
The “No” column is dropped and then clearer names are specified for each column. Finally, the NA values are replaced with “0” values and the first 24 hours are removed.
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from pandas import read_csv from datetime import datetime # load data def parse(x): return datetime.strptime(x, ‘%Y %m %d %H‘) dataset = read_csv(‘raw.csv‘, parse_dates = [[‘year‘, ‘month‘, ‘day‘, ‘hour‘]], index_col=0, date_parser=parse) dataset.drop(‘No‘, axis=1, inplace=True) # manually specify column names dataset.columns = [‘pollution‘, ‘dew‘, ‘temp‘, ‘press‘, ‘wnd_dir‘, ‘wnd_spd‘, ‘snow‘, ‘rain‘] dataset.index.name = ‘date‘ # mark all NA values with 0 dataset[‘pollution‘].fillna(0, inplace=True) # drop the first 24 hours dataset = dataset[24:] # summarize first 5 rows print(dataset.head(5)) # save to file dataset.to_csv(‘pollution.csv‘) |
Running the example prints the first 5 rows of the transformed dataset and saves the dataset to “pollution.csv“.
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pollution dew temp press wnd_dir wnd_spd snow rain date 2010-01-02 00:00:00 129.0 -16 -4.0 1020.0 SE 1.79 0 0 2010-01-02 01:00:00 148.0 -15 -4.0 1020.0 SE 2.68 0 0 2010-01-02 02:00:00 159.0 -11 -5.0 1021.0 SE 3.57 0 0 2010-01-02 03:00:00 181.0 -7 -5.0 1022.0 SE 5.36 1 0 2010-01-02 04:00:00 138.0 -7 -5.0 1022.0 SE 6.25 2 0 |
Now that we have the data in an easy-to-use form, we can create a quick plot of each series and see what we have.
The code below loads the new “pollution.csv” file and plots each series as a separate subplot, except wind speed dir, which is categorical.
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from pandas import read_csv from matplotlib import pyplot # load dataset dataset = read_csv(‘pollution.csv‘, header=0, index_col=0) values = dataset.values # specify columns to plot groups = [0, 1, 2, 3, 5, 6, 7] i = 1 # plot each column pyplot.figure() for group in groups: pyplot.subplot(len(groups), 1, i) pyplot.plot(values[:, group]) pyplot.title(dataset.columns[group], y=0.5, loc=‘right‘) i += 1 pyplot.show() |
Running the example creates a plot with 7 subplots showing the 5 years of data for each variable.
Line Plots of Air Pollution Time Series
3. Multivariate LSTM Forecast Model
In this section, we will fit an LSTM to the problem.
LSTM Data Preparation
The first step is to prepare the pollution dataset for the LSTM.
This involves framing the dataset as a supervised learning problem and normalizing the input variables.
We will frame the supervised learning problem as predicting the pollution at the current hour (t) given the pollution measurement and weather conditions at the prior time step.
This formulation is straightforward and just for this demonstration. Some alternate formulations you could explore include:
- Predict the pollution for the next hour based on the weather conditions and pollution over the last 24 hours.
- Predict the pollution for the next hour as above and given the “expected” weather conditions for the next hour.
We can transform the dataset using the series_to_supervised() function developed in the blog post:
First, the “pollution.csv” dataset is loaded. The wind speed feature is label encoded (integer encoded). This could further be one-hot encoded in the future if you are interested in exploring it.
Next, all features are normalized, then the dataset is transformed into a supervised learning problem. The weather variables for the hour to be predicted (t) are then removed.
The complete code listing is provided below.
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# convert series to supervised learning def series_to_supervised(data, n_in=1, n_out=1, dropnan=True): n_vars = 1 if type(data) is list else data.shape[1] df = DataFrame(data) cols, names = list(), list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) names += [(‘var%d(t-%d)‘ % (j+1, i)) for j in range(n_vars)] # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) if i == 0: names += [(‘var%d(t)‘ % (j+1)) for j in range(n_vars)] else: names += [(‘var%d(t+%d)‘ % (j+1, i)) for j in range(n_vars)] # put it all together agg = concat(cols, axis=1) agg.columns = names # drop rows with NaN values if dropnan: agg.dropna(inplace=True) return agg # load dataset dataset = read_csv(‘pollution.csv‘, header=0, index_col=0) values = dataset.values # integer encode direction encoder = LabelEncoder() values[:,4] = encoder.fit_transform(values[:,4]) # ensure all data is float values = values.astype(‘float32‘) # normalize features scaler = MinMaxScaler(feature_range=(0, 1)) scaled = scaler.fit_transform(values) # frame as supervised learning reframed = series_to_supervised(scaled, 1, 1) # drop columns we don‘t want to predict reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True) print(reframed.head()) |
Running the example prints the first 5 rows of the transformed dataset. We can see the 8 input variables (input series) and the 1 output variable (pollution level at the current hour).
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var1(t-1) var2(t-1) var3(t-1) var4(t-1) var5(t-1) var6(t-1) \ 1 0.129779 0.352941 0.245902 0.527273 0.666667 0.002290 2 0.148893 0.367647 0.245902 0.527273 0.666667 0.003811 3 0.159960 0.426471 0.229508 0.545454 0.666667 0.005332 4 0.182093 0.485294 0.229508 0.563637 0.666667 0.008391 5 0.138833 0.485294 0.229508 0.563637 0.666667 0.009912 var7(t-1) var8(t-1) var1(t) 1 0.000000 0.0 0.148893 2 0.000000 0.0 0.159960 3 0.000000 0.0 0.182093 4 0.037037 0.0 0.138833 5 0.074074 0.0 0.109658 |
This data preparation is simple and there is more we could explore. Some ideas you could look at include:
- One-hot encoding wind speed.
- Making all series stationary with differencing and seasonal adjustment.
- Providing more than 1 hour of input time steps.
This last point is perhaps the most important given the use of Backpropagation through time by LSTMs when learning sequence prediction problems.
Define and Fit Model
In this section, we will fit an LSTM on the multivariate input data.
First, we must split the prepared dataset into train and test sets. To speed up the training of the model for this demonstration, we will only fit the model on the first year of data, then evaluate it on the remaining 4 years of data. If you have time, consider exploring the inverted version of this test harness.
The example below splits the dataset into train and test sets, then splits the train and test sets into input and output variables. Finally, the inputs (X) are reshaped into the 3D format expected by LSTMs, namely [samples, timesteps, features].
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# split into train and test sets values = reframed.values n_train_hours = 365 * 24 train = values[:n_train_hours, :] test = values[n_train_hours:, :] # split into input and outputs train_X, train_y = train[:, :-1], train[:, -1] test_X, test_y = test[:, :-1], test[:, -1] # reshape input to be 3D [samples, timesteps, features] train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1])) test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1])) print(train_X.shape, train_y.shape, test_X.shape, test_y.shape) |
Running this example prints the shape of the train and test input and output sets with about 9K hours of data for training and about 35K hours for testing.
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(8760, 1, 8) (8760,) (35039, 1, 8) (35039,) |
Now we can define and fit our LSTM model.
We will define the LSTM with 50 neurons in the first hidden layer and 1 neuron in the output layer for predicting pollution. The input shape will be 1 time step with 8 features.
We will use the Mean Absolute Error (MAE) loss function and the efficient Adam version of stochastic gradient descent.
The model will be fit for 50 training epochs with a batch size of 72. Remember that the internal state of the LSTM in Keras is reset at the end of each batch, so an internal state that is a function of a number of days may be helpful (try testing this).
Finally, we keep track of both the training and test loss during training by setting the validation_data argument in the fit() function. At the end of the run both the training and test loss are plotted.
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# design network model = Sequential() model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]))) model.add(Dense(1)) model.compile(loss=‘mae‘, optimizer=‘adam‘) # fit network history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False) # plot history pyplot.plot(history.history[‘loss‘], label=‘train‘) pyplot.plot(history.history[‘val_loss‘], label=‘test‘) pyplot.legend() pyplot.show() |
Evaluate Model
After the model is fit, we can forecast for the entire test dataset.
We combine the forecast with the test dataset and invert the scaling. We also invert scaling on the test dataset with the expected pollution numbers.
With forecasts and actual values in their original scale, we can then calculate an error score for the model. In this case, we calculate the Root Mean Squared Error (RMSE) that gives error in the same units as the variable itself.
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# make a prediction yhat = model.predict(test_X) test_X = test_X.reshape((test_X.shape[0], test_X.shape[2])) # invert scaling for forecast inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1) inv_yhat = scaler.inverse_transform(inv_yhat) inv_yhat = inv_yhat[:,0] # invert scaling for actual test_y = test_y.reshape((len(test_y), 1)) inv_y = concatenate((test_y, test_X[:, 1:]), axis=1) inv_y = scaler.inverse_transform(inv_y) inv_y = inv_y[:,0] # calculate RMSE rmse = sqrt(mean_squared_error(inv_y, inv_yhat)) print(‘Test RMSE: %.3f‘ % rmse) |
Complete Example
The complete example is listed below.
NOTE: This example assumes you have prepared the data correctly, e.g. converted the downloaded “raw.csv” to the prepared “pollution.csv“. See the first part of this tutorial.
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from math import sqrt from numpy import concatenate from matplotlib import pyplot from pandas import read_csv from pandas import DataFrame from pandas import concat from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import LabelEncoder from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM # convert series to supervised learning def series_to_supervised(data, n_in=1, n_out=1, dropnan=True): n_vars = 1 if type(data) is list else data.shape[1] df = DataFrame(data) cols, names = list(), list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) names += [(‘var%d(t-%d)‘ % (j+1, i)) for j in range(n_vars)] # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) if i == 0: names += [(‘var%d(t)‘ % (j+1)) for j in range(n_vars)] else: names += [(‘var%d(t+%d)‘ % (j+1, i)) for j in range(n_vars)] # put it all together agg = concat(cols, axis=1) agg.columns = names # drop rows with NaN values if dropnan: agg.dropna(inplace=True) return agg # load dataset dataset = read_csv(‘pollution.csv‘, header=0, index_col=0) values = dataset.values # integer encode direction encoder = LabelEncoder() values[:,4] = encoder.fit_transform(values[:,4]) # ensure all data is float values = values.astype(‘float32‘) # normalize features scaler = MinMaxScaler(feature_range=(0, 1)) scaled = scaler.fit_transform(values) # frame as supervised learning reframed = series_to_supervised(scaled, 1, 1) # drop columns we don‘t want to predict reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True) print(reframed.head()) # split into train and test sets values = reframed.values n_train_hours = 365 * 24 train = values[:n_train_hours, :] test = values[n_train_hours:, :] # split into input and outputs train_X, train_y = train[:, :-1], train[:, -1] test_X, test_y = test[:, :-1], test[:, -1] # reshape input to be 3D [samples, timesteps, features] train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1])) test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1])) print(train_X.shape, train_y.shape, test_X.shape, test_y.shape) # design network model = Sequential() model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]))) model.add(Dense(1)) model.compile(loss=‘mae‘, optimizer=‘adam‘) # fit network history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False) # plot history pyplot.plot(history.history[‘loss‘], label=‘train‘) pyplot.plot(history.history[‘val_loss‘], label=‘test‘) pyplot.legend() pyplot.show() # make a prediction yhat = model.predict(test_X) test_X = test_X.reshape((test_X.shape[0], test_X.shape[2])) # invert scaling for forecast inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1) inv_yhat = scaler.inverse_transform(inv_yhat) inv_yhat = inv_yhat[:,0] # invert scaling for actual test_y = test_y.reshape((len(test_y), 1)) inv_y = concatenate((test_y, test_X[:, 1:]), axis=1) inv_y = scaler.inverse_transform(inv_y) inv_y = inv_y[:,0] # calculate RMSE rmse = sqrt(mean_squared_error(inv_y, inv_yhat)) print(‘Test RMSE: %.3f‘ % rmse) |
Running the example first creates a plot showing the train and test loss during training.
Interestingly, we can see that test loss drops below training loss. The model may be overfitting the training data. Measuring and plotting RMSE during training may shed more light on this.
Line Plot of Train and Test Loss from the Multivariate LSTM During Training
The Train and test loss are printed at the end of each training epoch. At the end of the run, the final RMSE of the model on the test dataset is printed.
We can see that the model achieves a respectable RMSE of 26.496, which is lower than an RMSE of 30 found with a persistence model.
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... Epoch 46/50 0s - loss: 0.0143 - val_loss: 0.0133 Epoch 47/50 0s - loss: 0.0143 - val_loss: 0.0133 Epoch 48/50 0s - loss: 0.0144 - val_loss: 0.0133 Epoch 49/50 0s - loss: 0.0143 - val_loss: 0.0133 Epoch 50/50 0s - loss: 0.0144 - val_loss: 0.0133 Test RMSE: 26.496 |
This model is not tuned. Can you do better?
Let me know your problem framing, model configuration, and RMSE in the comments below.
Update: Train On Multiple Lag Timesteps Example
There have been many requests for advice on how to adapt the above example to train the model on multiple previous time steps.
I had tried this and a myriad of other configurations when writing the original post and decided not to include them because they did not lift model skill.
Nevertheless, I have included this example below as reference template that you could adapt for your own problems.
The changes needed to train the model on multiple previous time steps are quite minimal, as follows:
First, you must frame the problem suitably when calling series_to_supervised(). We will use 3 hours of data as input. Also note, we no longer explictly drop the columns from all of the other fields at ob(t).
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# specify the number of lag hours n_hours = 3 n_features = 8 # frame as supervised learning reframed = series_to_supervised(scaled, n_hours, 1) |
Next, we need to be more careful in specifying the column for input and output.
We have 3 * 8 + 8 columns in our framed dataset. We will take 3 * 8 or 24 columns as input for the obs of all features across the previous 3 hours. We will take just the pollution variable as output at the following hour, as follows:
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# split into input and outputs n_obs = n_hours * n_features train_X, train_y = train[:, :n_obs], train[:, -n_features] test_X, test_y = test[:, :n_obs], test[:, -n_features] print(train_X.shape, len(train_X), train_y.shape) |
Next, we can reshape our input data correctly to reflect the time steps and features.
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# reshape input to be 3D [samples, timesteps, features] train_X = train_X.reshape((train_X.shape[0], n_hours, n_features)) test_X = test_X.reshape((test_X.shape[0], n_hours, n_features)) |
Fitting the model is the same.
The only other small change is in how to evaluate the model. Specifically, in how we reconstruct the rows with 8 columns suitable for reversing the scaling operation to get the y and yhat back into the original scale so that we can calculate the RMSE.
The gist of the change is that we concatenate the y or yhat column with the last 7 features of the test dataset in order to inverse the scaling, as follows:
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# invert scaling for forecast inv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1) inv_yhat = scaler.inverse_transform(inv_yhat) inv_yhat = inv_yhat[:,0] # invert scaling for actual test_y = test_y.reshape((len(test_y), 1)) inv_y = concatenate((test_y, test_X[:, -7:]), axis=1) inv_y = scaler.inverse_transform(inv_y) inv_y = inv_y[:,0] |
We can tie all of these modifications to the above example together. The complete example of multvariate time series forecasting with multiple lag inputs is listed below:
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from math import sqrt from numpy import concatenate from matplotlib import pyplot from pandas import read_csv from pandas import DataFrame from pandas import concat from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import LabelEncoder from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM # convert series to supervised learning def series_to_supervised(data, n_in=1, n_out=1, dropnan=True): n_vars = 1 if type(data) is list else data.shape[1] df = DataFrame(data) cols, names = list(), list() # input sequence (t-n, ... t-1) for i in range(n_in, 0, -1): cols.append(df.shift(i)) names += [(‘var%d(t-%d)‘ % (j+1, i)) for j in range(n_vars)] # forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) if i == 0: names += [(‘var%d(t)‘ % (j+1)) for j in range(n_vars)] else: names += [(‘var%d(t+%d)‘ % (j+1, i)) for j in range(n_vars)] # put it all together agg = concat(cols, axis=1) agg.columns = names # drop rows with NaN values if dropnan: agg.dropna(inplace=True) return agg # load dataset dataset = read_csv(‘pollution.csv‘, header=0, index_col=0) values = dataset.values # integer encode direction encoder = LabelEncoder() values[:,4] = encoder.fit_transform(values[:,4]) # ensure all data is float values = values.astype(‘float32‘) # normalize features scaler = MinMaxScaler(feature_range=(0, 1)) scaled = scaler.fit_transform(values) # specify the number of lag hours n_hours = 3 n_features = 8 # frame as supervised learning reframed = series_to_supervised(scaled, n_hours, 1) print(reframed.shape) # split into train and test sets values = reframed.values n_train_hours = 365 * 24 train = values[:n_train_hours, :] test = values[n_train_hours:, :] # split into input and outputs n_obs = n_hours * n_features train_X, train_y = train[:, :n_obs], train[:, -n_features] test_X, test_y = test[:, :n_obs], test[:, -n_features] print(train_X.shape, len(train_X), train_y.shape) # reshape input to be 3D [samples, timesteps, features] train_X = train_X.reshape((train_X.shape[0], n_hours, n_features)) test_X = test_X.reshape((test_X.shape[0], n_hours, n_features)) print(train_X.shape, train_y.shape, test_X.shape, test_y.shape) # design network model = Sequential() model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]))) model.add(Dense(1)) model.compile(loss=‘mae‘, optimizer=‘adam‘) # fit network history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False) # plot history pyplot.plot(history.history[‘loss‘], label=‘train‘) pyplot.plot(history.history[‘val_loss‘], label=‘test‘) pyplot.legend() pyplot.show() # make a prediction yhat = model.predict(test_X) test_X = test_X.reshape((test_X.shape[0], n_hours*n_features)) # invert scaling for forecast inv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1) inv_yhat = scaler.inverse_transform(inv_yhat) inv_yhat = inv_yhat[:,0] # invert scaling for actual test_y = test_y.reshape((len(test_y), 1)) inv_y = concatenate((test_y, test_X[:, -7:]), axis=1) inv_y = scaler.inverse_transform(inv_y) inv_y = inv_y[:,0] # calculate RMSE rmse = sqrt(mean_squared_error(inv_y, inv_yhat)) print(‘Test RMSE: %.3f‘ % rmse) |
The model is fit as before in a minute or two.
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... Epoch 45/50 1s - loss: 0.0143 - val_loss: 0.0154 Epoch 46/50 1s - loss: 0.0143 - val_loss: 0.0148 Epoch 47/50 1s - loss: 0.0143 - val_loss: 0.0152 Epoch 48/50 1s - loss: 0.0143 - val_loss: 0.0151 Epoch 49/50 1s - loss: 0.0143 - val_loss: 0.0152 Epoch 50/50 1s - loss: 0.0144 - val_loss: 0.0149 |
A plot of train and test loss over the epochs is plotted.
Plot of Loss on the Train and Test Datasets
Finally, the Test RMSE is printed, not really showing any advantage in skill, at least on this problem.
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Test RMSE: 27.177 |
I would add that the LSTM does not appear to be suitable for autoregression type problemsand that you may be better off exploring an MLP with a large window.
I hope this example helps you with your own time series forecasting experiments.
原文地址：https://www.cnblogs.com/luofeel/p/9475388.html