# Why does the smallest LSTM I can make perform so well on this time series forecast?

So I've been playing with some different forecasting methods on a data set that I have done some more basic analyses for in the past. Without going into to much detail, it's population data over time driven by a variety of unknown factors. I don't expect to be able to predict it very well, and, historically, I've seen MAPE values in the 25% range using more naive models like ARIMA (though even Prophet models haven't worked great). So today, for fun, I decided to try the LSTM code from https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ to see how it'd do. Suffice it to say, it performed way too well. So well, I assume I've done something wrong. For fun, I decided to restrict the number of parameters to the smallest possible number. This model literally has 9 parameters:

I disabled the bias parameters on the LSTM and Dense layers, and I specified a linear activation function. I don't think it's possible to have LSTM+Dense with fewer parameters.

To make it harder on the model, I trained it for 1 step and set the lookback to 1. The resulting code looks like this:

# LSTM for international airline passengers problem with memory
import numpy
import matplotlib.pyplot as plt
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
# convert an array of values into a dataset matrix
def create_dataset(ds, lb=1):
dataX, dataY = [], []
for i in range(len(ds)-lb-1):
a = ds[i:(i+lb), 0]
dataX.append(a)
dataY.append(ds[i + lb, 0])
return numpy.array(dataX), numpy.array(dataY)
# fix random seed for reproducibility
numpy.random.seed(7)
# load the dataset
dataframe = df_forecasting
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
scaler.fit([[0], [50]]) # Maximum expected population is ~50 for problem-related reasons
dataset = scaler.transform(dataset)
# split into train and test sets
train_size = list(df_forecasting.index > pd.to_datetime('20200101')).index(True) # I want to see about forecasting 2020
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))
# create and fit the LSTM network
batch_size = 1
model = Sequential()
model.add(LSTM(1, batch_input_shape=(batch_size, look_back, 1), stateful=True, use_bias=False))
model.add(Dense(1, activation='linear', use_bias=False))
model.compile(loss='mean_squared_error', optimizer='adam')
for i in range(1):
model.fit(trainX, trainY, epochs=1, batch_size=batch_size, verbose=2, shuffle=False)
model.reset_states()
# make predictions
trainPredict = model.predict(trainX, batch_size=batch_size)
model.reset_states()
testPredict = model.predict(testX, batch_size=batch_size)
# invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.figure(figsize=(15, 10))
plt.plot(scaler.inverse_transform(dataset), c="g", label="Original Data")
plt.plot(trainPredictPlot, c="b", label="Train Prediction")
plt.plot(testPredictPlot, c="r", label="Test Prediction")
plt.legend()
plt.show()
print(model.summary())


So this model is about as impoverished as I can think to make it for an LSTM, and yet, here are the results:

I'm stumped. I've traced through the code and I simply don't understand how it's working so well compared to other models I've tried. One theory I have is that this data is actually a moving average, but it's a causal average, so I don't see how that could leak information. Even if it could, 9 parameters can use that?

Would love to understand better what's going on here. The map is 17, which I find incredible compared to the other things I tried. When I un-break the model (101 params, 10 steps, 7day lookback) I get the MAPE down to 9. I think it would go substantially lower if I let it, but I want to believe first...

## 1 Answer

Is it possible that your model is just mimicking the output of the previous timestep? The predicted population is trailing the true population by 1 time-step. The model is just predicting a value close to the previous population it sees, as it feels this is the best prediction for the next price. For e.g. https://towardsdatascience.com/how-not-to-predict-stock-prices-with-lstms-a51f564ccbca

• This definitely seems to be what's happening. I imagine this may be made a bit worse by the fact that each point is a moving average, so the variability is artificially suppressed. What is the typical mitigation for this? Predicting a longer forecast horizon per time step? I see the article leaves the answer somewhat open, but I'd love some opinions, if you have any. Commented Nov 27, 2020 at 23:02
• Not at the moment, but I am planning to look into time series model in little more detail in the coming days. So I will definitely revert over time if I get the answer. You may want to monitor: kaggle.com/c/jane-street-market-prediction/overview as this is a new time series competition and the discussions and notebooks there are gold standard Commented Nov 28, 2020 at 13:37