Time series forecasting is a critical task that can be applied to multiple fields, including finance, meteorology, and transportation. In this blog, we will explore how to use Python for time series forecasting, focusing specifically on three specific application scenarios: stock prices, weather data, and traffic flow. We will detail the basic concepts, data preparation, modeling techniques, and evaluation methods of time series forecasting.
What is time series forecasting?
A time series is a collection of data points arranged in time order. Time series forecasting is the prediction of values at future time points based on known past data. This task has wide applications in many fields, such as:
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Stock Price Forecasting : Investors and financial institutions use time series forecasting to predict the prices of stocks, bonds, and other financial assets to make investment decisions.
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Meteorological data forecasting : Meteorologists use time series models to predict weather data such as temperature, humidity, and precipitation to provide weather forecasts.
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Traffic flow forecasting : City planners and traffic management authorities use time series forecasting to predict traffic flow on roads to improve traffic management and planning.
In the following sections, we discuss in detail how time series forecasting is performed in these applications.
stock price prediction
Stock price prediction is one of the most challenging and important problems in the financial field. Investors try to make profits by predicting the future direction of stock prices. Here, we will introduce a basic method for stock price prediction using Python.
data preparation
First, we need to obtain historical stock price data. You can use the API of a financial data provider, such as Alpha Vantage or Yahoo Finance, or get the data from local data sources. Data typically includes dates and stock prices.
Let’s use Python’s Pandas library to load and visualize stock data:
import pandas as pd
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