Time Series Forecasting using Zerve AI and PatchTST model
Time series forecasting is a crucial tool in the world of data analytics and machine learning. It involves predicting future values based on past observations, and it finds applications in a wide range of fields, from finance and economics to weather forecasting and resource planning. In this blog post, we’ll explore the basics of time series forecasting, the key techniques used, and some popular algorithms.
What is Time Series Forecasting?
A time series is a sequence of data points collected at regular intervals over time. Time series forecasting aims to predict future values based on historical data. The data points in a time series are typically plotted on a graph, with time on the x-axis and the value of interest on the y-axis. By analyzing patterns in the data, we can make informed predictions about future values.
Key Concepts in Time Series Forecasting
- Trend: The trend component represents the long-term movement in the data. It can be upward, downward, or stable over time.
- Seasonality: Seasonality refers to the recurring patterns or cycles in the data that occur at regular intervals. For example, retail sales might exhibit seasonality with peaks during holiday seasons.
- Noise: Noise, also known as randomness or error, represents the unpredictable fluctuations in the data that are not explained by the trend or seasonality.
- Stationarity: A time series is said to be stationary if its statistical properties such as mean, variance, and autocorrelation remain constant over time. Stationarity is important for many time series forecasting models.
Time Series Forecasting Techniques
Moving Average
The moving average method calculates the average of a specific number of data points from the past and uses it to forecast future values. It is simple yet effective in smoothing out short-term fluctuations.
Exponential Smoothing
Exponential smoothing gives more weight to recent data points while forecasting. It is useful for capturing trends and seasonality in the data.
Autoregressive Integrated Moving Average (ARIMA)
ARIMA is a popular and powerful technique for time series forecasting. It models the time series as a combination of autoregressive (AR), differencing (I), and moving average (MA) terms.
Seasonal Decomposition of Time Series (STL)
STL decomposes a time series into trend, seasonal, and residual components, making it easier to analyze and forecast each component separately.
Deep Learning Models
Deep learning models, particularly recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks, have shown promising results in time series forecasting. They can capture complex patterns in the data and make accurate predictions. Nowadys newer models based on transformer networks are coming up which are significantly beating the traditional LSTM models.
But remember, forecasting is an estimate, not a guarantee. The accuracy depends on several factors:
- Data quality: Garbage in, garbage out! Ensure your data is clean, consistent, and relevant.
- Model selection: Choosing the right “teacher” is crucial. Consider the nature of your data and the type of predictions you need.
- External factors: Real-world events can throw wrenches in the forecast. Be mindful of potential disruptions.
Installs and Imports
We will be using the neuralforecast library for our modelling.
Dataset Loading
We will use the store sales dataset from kaggle (click here).
Dataset Background
Forecasts aren’t just for meteorologists. Governments forecast economic growth. Scientists attempt to predict the future population. And businesses forecast product demand — a common task of professional data scientists. Forecasts are especially relevant to brick-and-mortar grocery stores, which must dance delicately with how much inventory to buy. Predict a little over, and grocers are stuck with overstocked, perishable goods. Guess a little under, and popular items quickly sell out, leading to lost revenue and upset customers. More accurate forecasting, thanks to machine learning, could help ensure retailers please customers by having just enough of the right products at the right time.
Current subjective forecasting methods for retail have little data to back them up and are unlikely to be automated. The problem becomes even more complex as retailers add new locations with unique needs, new products, ever-transitioning seasonal tastes, and unpredictable product marketing. The aim is to build a model that more accurately predicts the unit sales for thousands of items sold at different Favorita stores.
Model Building
For our modelling we will make use of the PatchTST model from the neuralforecast library.
Traditional forecasting models often struggle with long-term predictions and dealing with multiple data channels simultaneously. Transformers, known for their parallel processing prowess, offered a glimmer of hope. However, limitations in handling long sequences and channel independence persisted. PatchTST addresses these challenges head-on.
Here’s what sets it apart:
- Patching: It divides lengthy time series into manageable “patches,” enabling efficient processing and capturing fine-grained patterns. Channel Independence: Instead of treating all channels as one, PatchTST analyzes them individually, unlocking unique insights within each data stream.
- Transformer Power: Leveraging Transformer architecture, PatchTST excels at identifying complex relationships and dependencies within the data, leading to more accurate forecasts.
The Benefits of PatchTST:
- Improved Accuracy: Compared to other Transformer-based models, PatchTST demonstrably reduces error rates, especially for long-term forecasting.
- Scalability: Its ability to handle multiple channels seamlessly makes it ideal for complex, multivariate datasets.
- Efficiency: Processing data in patches leads to faster training and inference times, saving computational resources. Beyond Forecasting:
The beauty of PatchTST lies in its versatility. It can be used for:
- Self-supervised learning: By learning from unlabeled data, it enhances its forecasting capabilities even with limited labeled data.
- Transfer learning: Pre-trained on one dataset, it can be fine-tuned for new tasks, accelerating the learning process.
Zerve.ai: The Game changer
For data scientists and AI practitioners, the development process can often feel like juggling multiple tools and battling clunky workflows. That’s where Zerve.ai steps in, aiming to revolutionize the data science landscape with a unified developer space.
Forget the limitations of traditional notebooks. Zerve.ai offers a powerful environment that seamlessly integrates:
- Python, R, SQL, and Markdown: Work with your preferred languages and formats in one cohesive workspace.
- Interactive coding and visualization: Explore your data, write code, and visualize results simultaneously, fostering deeper insights.
- Automated cloud infrastructure: Spin up resources on-demand, eliminating infrastructure management headaches.
- Real-time collaboration: Share, comment, and version control your work with your team, streamlining collaboration.
- Deployment flexibility: Export your models as APIs, apps, dockers or presentations, seamlessly delivering insights to stakeholders. Moreover, you can directly deploy models from the zerve workspace without any hassle. This all allows for a seamless handover of the models from the data scientist to the production engineer.
Zerve.ai goes beyond being a fancy notebook. It empowers data scientists with game-changing features:
- True language interoperability: Connect code blocks from different languages, creating powerful workflows that combine strengths.
- Fine-grained resource allocation: Allocate compute resources precisely, optimizing costs and performance.
- Persistent results: Never lose your work again. All computations and visualizations are automatically saved and reproducible.
- Open-source integrations: Leverage your favorite tools and libraries within the Zerve.ai ecosystem.
We built this whole notebook in Zerve.ai seamlessly.
Along with this amazingly integrated workspace zerve.ai platform also has other features like the code and variables mode where the user can focus on one particular code section and see all the input and output variables for easy debugging and pipeline generation.
