How to Move from Data Analysis in Python to Forecasting and Time Series Analysis
Python continues to top the list of must-have skills for data professionals, with nearly 80% of hiring managers expecting it, no matter the experience level. Once you’ve built a solid foundation — cleaning, exploring, and visualizing historical data — you’ll probably start wondering: what comes after this?
The answer is simple: learning to look ahead. That means moving from analyzing “what happened” to “what’s likely to happen next” with Python time series forecasting. In this guide, we explain the transition with step-by-step examples and easy-to-read code snippets.
From Looking Back to Looking Forward
Think of data analysis as driving with your eyes on the rearview mirror. You can clearly see what you’ve already passed through: the bumps, the turns, the traffic patterns.
Forecasting, in turn, is like looking through the windshield. You use your understanding of the road behind to anticipate the conditions ahead and steer in the right direction.
In business, the transition from “rearview” to “windshield” thinking is even more tangible:
- Retail. Instead of only reporting last month’s top-selling items, forecasting with Python helps predict which products are likely to sell next month.
- Healthcare. Analysis might reveal that flu season often peaks in late fall. Forecasting goes further by estimating the exact timing and scale of this year’s outbreak, so clinics can prepare beds, staff, and medication in line with the expected demand.
- Logistics. Analysis shows where and when delivery bottlenecks happened in the past. Forecasting, on the other hand, can predict upcoming shipment volumes and demand spikes, allowing companies to adjust capacity, routes, and staffing before delays occur.
Preparing Data for Forecasting
Before you start forecasting with Python, you need the right kind of data. And the most critical component here is time.
Forecasting is based on observations tied to specific dates, whether daily, weekly, or monthly. Raw data, however, is rarely perfect. You might be dealing with missing dates, irregular intervals, or inconsistent time formats. And without a proper time reference, forecasting models can’t work.
The first step is to align all the data you have into a clean, continuous timeline. Luckily, Python helps with that through:
- Pandas. A popular Python library, it can recognize dates, fill gaps, resample data into regular intervals, and organize your dataset before forecasting models use it.
- Matplotlib. Once your data is structured, Matplotlib helps visualize it. The tool turns raw time-stamped values into meaningful visual representations, revealing specific trends.
Python Tools That Make Forecasting Easier
Pandas and Matplotlib are great for preparing time series data. But which tools should you use to actually start forecasting? Here are a few beginner-friendly libraries:
- Statsmodels. As the name suggests, it’s a go-to library for traditional statistical methods, and it’s commonly used for time series forecasting, including auto-regressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA).
- Prophet. Developed by Meta, it’s a procedure for time series forecasting that breaks down your data into different components, including non-linear trends with yearly, weekly, and daily patterns.
- Scikit-learn. While best known for using Python for AI and ML technologies, it offers tools for regression models that can be adapted for forecasting.
Beyond the basics, the transition from data analysis to forecasting with Python can also be done with advanced libraries:
- Long short-term memory networks (LSTMs). A recurrent neural network (RNN) that can handle sequential data.
- Transformers. Initially developed for natural language processing, they can also be applied to time series forecasting.
How to Forecast Time Series Data in Python: A Step-by-Step Example
Now, let’s talk practice. Imagine you’ve been collecting daily sales data from your shop for two years. Your goal is to use Python time series analysis to predict what might happen in the next week or month.
The Process Looks Like This
A typical workflow for forecasting with Python includes:
Loading the data. Load your dataset into Python from a CSV file. Make sure each row includes a date and the corresponding sales number.
Cleaning and visualizing. Ensure all dates are in order and add any missing ones. Next, plot the data to see trends, such as growth, weekend dips, or seasonal spikes.
Applying the forecasting model. Use either ARIMA, where you choose parameters and tune the model yourself, or Prophet, which automatically handles trends, seasonality, and holidays.
Getting predictions. Once the model is trained, conduct forecasting. For example, predict the next 7 days for short-term planning or the next 30 days for a broader outlook.
ARIMA
from statsmodels.tsa.arima.model import ARIMA
# fit a simple ARIMA model
model = ARIMA(data, order=(1,1,1))
model_fit = model.fit()
# forecast the next 7 steps
forecast = model_fit.forecast(steps=7)
print(forecast)
ARIMA is one of the most established forecasting techniques. It works by analyzing how today’s values relate to yesterday’s and then uses that relationship to project tomorrow’s. In our example, the model looks at the shop’s sales history and generates a forecast for the next seven days.
Prophet
from prophet import Prophet
import pandas as
# prepare data (two columns: ds = dates, y = values)
df = pd.DataFrame({"ds": dates, "y": values})
# fit and forecast model = Prophet().fit(df) future = model.make_future_dataframe(periods=30) forecast = model.predict(future) print(forecast[['ds','yhat']].tail())
Industries Where Forecasting Creates Business Value
Now that you know how to use predictive modeling with Python, let’s consider where you can use it. Here are several industries that can benefit:
- Retail. Rely on demand forecasting for seasonal products, proper staff scheduling, and smarter pricing decisions.
- Logistics. Use time series modeling in Python to estimate parcel volumes, optimize routes, and minimize delays.
- Manufacturing. Leverage forecasting to plan production, control inventory, and order raw materials ahead of time.
- Aviation. Forecast passenger flows to allocate crews, plan fuel usage, and optimize flight schedules.
- Healthcare. Use forecasting to anticipate patient appointments, predict bed occupancy, and allocate staff or equipment.
Conclusion
Forecasting with Python means moving from simple data analysis, which looks into the past, to predicting the future. As you’ve seen in our guide, the process takes several steps: preparing time series data, visualizing it, applying the right tools, and, ultimately, getting your predictions ready.
In case you’re looking to transition from data analysis to Python time series analysis, contact Integrio Systems. We also use AI in retail analytics and other industries to make even smarter predictions.
FAQ
Time series analysis involves studying the data collected over time, whether hourly, daily, weekly, or monthly. It considers the order of values and uncovers patterns within them, such as seasonality or unexpected changes.
Data analysis takes into account the past data and draws conclusions based on it. Python time series forecasting considers past data, too, but it also uses it to make predictions.
Not necessarily. You can start forecasting with Python by using simple, beginner-friendly tools, such as Prophet or ARIMA.
There are three options beginners can use to start forecasting with Python. They include Statsmodels, Prophet, and Scikit-learn.
You should consider advanced methods like LSTMs or Transformers when your data is highly complex, has long-term dependencies, or when traditional models no longer provide accurate forecasts. These methods are more powerful but also require more data, computation, and expertise.
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