From Text to Time Series: An Engineer's Look at TimesFM
TimesFM (Time Series Foundation Model) is a pre-trained model for time series forecasting, developed by Google Research. Think of it like a large language model (LLM) for time series data. Just as an LLM, like GPT-4, learns from a massive amount of text to understand language patterns, TimesFM has been trained on a huge, diverse dataset of time series data. This pre-training allows it to understand complex temporal patterns and dependencies that are common across various types of time series, such as sales, stock prices, or sensor readings.
As a software engineer, TimesFM can significantly streamline your workflow and improve the accuracy of your forecasting models. Here are some key benefits
No more tedious feature engineering
Traditionally, time series forecasting requires a lot of manual work to create features like moving averages, lagged variables, or seasonal components. TimesFM's pre-trained nature eliminates much of this effort. It already understands these patterns, so you can focus on building your application instead of worrying about feature engineering.
Reduced need for large datasets
Training a high-quality forecasting model from scratch often requires a huge amount of historical data. TimesFM, because it's already pre-trained, can achieve excellent results even with limited data. This is a game-changer for many projects where data is scarce.
Faster development and deployment
With TimesFM, you can quickly build a forecasting solution without spending weeks or months on model training. You can simply load the pre-trained model and fine-tune it on your specific data, which is much faster than training a model from scratch. This allows for rapid prototyping and deployment.
Improved accuracy
Due to its extensive training on a wide range of time series data, TimesFM often outperforms traditional forecasting methods and even custom-trained models, especially on complex datasets.
The easiest way to get started with TimesFM is by using the google-research/timesfm library. You'll need Python and a few common libraries like pandas and numpy.
First, install the library and its dependencies using pip.
pip install timesfm
Here's a simple example showing how to use TimesFM to make a prediction. This code assumes you have a time series dataset stored in a pandas DataFrame.
import numpy as np
import pandas as pd
from timesfm import TimesFm
# Initialize TimesFM
# You can use a GPU if available for faster computation.
timesfm = TimesFm(
context_len=512, # The number of historical data points to consider.
horizon_len=24, # The number of future data points to predict.
input_patch_len=32, # Patch length for the model, a setting for model architecture.
output_patch_len=128, # Output patch length.
num_encoder_layers=12, # Number of layers in the encoder.
model_dims=640, # The dimensionality of the model's internal representation.
seed=123
)
# Load the pre-trained weights. This will download the model.
timesfm.load_weights()
# Let's create some sample data (replace this with your actual data)
# This data should have a shape of (num_series, num_data_points)
num_series = 1
num_points = 1000
data = np.random.rand(num_series, num_points).astype(np.float32)
# Make sure your data is in the correct format (float32 numpy array)
# It should be a 2D array: (number of time series, length of each series)
forecast_input = data[:, -512:] # Use the last 512 points as context
# Predict the next 24 data points
forecast_output, _, _ = timesfm.predict(
forecast_input,
np.zeros((num_series, 0)) # This is for future known covariates, we can leave it empty for this example.
)
# `forecast_output` will be a numpy array with the shape (num_series, horizon_len)
# In this case, (1, 24).
print("Forecasted values:")
print(forecast_output)
# You can now integrate this forecast into your application, e.g.,
# plotting it, saving it to a database, or using it for a real-time alert system.
When you initialize TimesFm, you'll see a few important parameters
context_len
This is the look-back window. It's the number of historical data points the model will use to make a prediction. A larger context length can sometimes improve accuracy, but it also increases computation time.
horizon_len
This is the prediction horizon. It's the number of future time steps you want to forecast.
model_dims and num_encoder_layers
These are architectural parameters that define the size and complexity of the model. For most use cases, you can stick with the defaults as they have been optimized for a wide range of problems.