Keras Forecasting Tutorial

In this tutorial, we will explore how to use Keras for time series forecasting. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow. It is designed to enable fast prototyping, straightforward experimentation, and production-ready deployment.

Prerequisites

Before diving into the tutorial, make sure you have the following prerequisites:

Basic knowledge of Python programming.
Understanding of neural networks and machine learning concepts.
Keras library installed. You can install it using pip:
```
pip install keras
```

Introduction to Time Series Forecasting

Time series forecasting is a technique used to predict future values based on historical data. It is widely used in various fields, such as finance, economics, and weather forecasting.

Building a Forecasting Model with Keras

Step 1: Data Preparation

First, we need to prepare our data. Let's assume we have a dataset containing daily temperature readings.

import numpy as np
import pandas as pd

# Load dataset
data = pd.read_csv('/path/to/your/dataset.csv')

# Preprocess the data
# (Code for preprocessing goes here)

Step 2: Model Architecture

Next, we will build the model architecture. For time series forecasting, a common approach is to use LSTM (Long Short-Term Memory) networks.

from keras.models import Sequential
from keras.layers import LSTM, Dense

# Define the model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(1))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

Step 3: Training the Model

Now, we can train our model using the training data.

# Train the model
model.fit(X_train, y_train, epochs=100, batch_size=32)

Step 4: Making Predictions

Finally, we can make predictions using the trained model.

# Make predictions
predictions = model.predict(X_test)