Improve the code to forecast the next 10 time steps
Aurélien Geron
parent
f8f2b9e4bb
commit
6c0cd5d2df
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@ -487,7 +487,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now let's create an RNN that predicts all 10 next values at once:"
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"Now let's use this model to predict the next 10 values. We first need to regenerate the sequences with 9 more time steps."
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]
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},
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{
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@ -505,11 +505,93 @@
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"X_test, Y_test = series[9000:, :n_steps], series[9000:, -10:, 0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now let's predict the next 10 values one by one:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = X_valid\n",
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"for step_ahead in range(10):\n",
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" y_pred_one = model.predict(X)[:, np.newaxis, :]\n",
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" X = np.concatenate([X, y_pred_one], axis=1)\n",
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"\n",
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"Y_pred = X[:, n_steps:, 0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"Y_pred.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.mean(keras.metrics.mean_squared_error(Y_valid, Y_pred))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's compare this performance with some baselines: naive predictions and a simple linear model:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"Y_naive_pred = Y_valid[:, -1:]\n",
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"np.mean(keras.metrics.mean_squared_error(Y_valid, Y_naive_pred))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(42)\n",
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"tf.random.set_seed(42)\n",
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"\n",
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"model = keras.models.Sequential([\n",
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" keras.layers.Flatten(input_shape=[50, 1]),\n",
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" keras.layers.Dense(10)\n",
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"])\n",
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"\n",
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"model.compile(loss=\"mse\", optimizer=\"adam\")\n",
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"history = model.fit(X_train, Y_train, epochs=20,\n",
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" validation_data=(X_valid, Y_valid))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now let's create an RNN that predicts all 10 next values at once:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(42)\n",
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"tf.random.set_seed(42)\n",
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@ -527,7 +609,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -540,7 +622,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -557,7 +639,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -578,7 +660,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -587,7 +669,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -603,14 +685,14 @@
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"def last_time_step_mse(Y_true, Y_pred):\n",
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" return keras.metrics.mean_squared_error(Y_true[:, -1], Y_pred[:, -1])\n",
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"\n",
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"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
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"model.compile(loss=\"mse\", optimizer=keras.optimizers.Adam(lr=0.01), metrics=[last_time_step_mse])\n",
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"history = model.fit(X_train, Y_train, epochs=20,\n",
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" validation_data=(X_valid, Y_valid))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -623,7 +705,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -640,7 +722,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -669,7 +751,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 42,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -678,7 +760,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 43,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -704,7 +786,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -732,7 +814,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -764,7 +846,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -792,7 +874,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 47,
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"metadata": {
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"scrolled": true
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},
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@ -812,79 +894,6 @@
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" validation_data=(X_valid, Y_valid))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.evaluate(X_valid, Y_valid)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(43)\n",
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"\n",
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"series = generate_time_series(1, 50 + 10)\n",
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"X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n",
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"Y_pred = model.predict(X_new)[:, -1][..., np.newaxis]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GRUs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"np.random.seed(42)\n",
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"tf.random.set_seed(42)\n",
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"\n",
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"model = keras.models.Sequential([\n",
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" keras.layers.GRU(20, return_sequences=True, input_shape=[None, 1]),\n",
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" keras.layers.GRU(20, return_sequences=True),\n",
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" keras.layers.TimeDistributed(keras.layers.Dense(10))\n",
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"])\n",
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"\n",
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"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
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"history = model.fit(X_train, Y_train, epochs=20,\n",
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" validation_data=(X_valid, Y_valid))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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@ -929,6 +938,79 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GRUs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"np.random.seed(42)\n",
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"tf.random.set_seed(42)\n",
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"\n",
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"model = keras.models.Sequential([\n",
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" keras.layers.GRU(20, return_sequences=True, input_shape=[None, 1]),\n",
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" keras.layers.GRU(20, return_sequences=True),\n",
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" keras.layers.TimeDistributed(keras.layers.Dense(10))\n",
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"])\n",
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"\n",
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"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
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"history = model.fit(X_train, Y_train, epochs=20,\n",
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" validation_data=(X_valid, Y_valid))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.evaluate(X_valid, Y_valid)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(43)\n",
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"\n",
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"series = generate_time_series(1, 50 + 10)\n",
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"X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n",
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"Y_pred = model.predict(X_new)[:, -1][..., np.newaxis]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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},
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{
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"cell_type": "code",
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"outputs": [],
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},
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{
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},
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},
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{
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"source": [
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"source": [
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"To be continued..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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