Overview of online forecasting technology and various applications and implementations

About Online Forecasting

Online Prediction (Online Prediction) is a method that uses models to make predictions in real time under conditions where data arrive sequentially.” Online learning, as described in “Overview of Online Learning, Various Algorithms, Application Examples, and Specific Implementations” is characterized by the fact that models are learned sequentially but the immediacy of model application is not clearly defined, while online prediction is characterized by the fact that predictions are made immediately upon the arrival of new data and the results are used. characteristics.

Online forecasting has the following characteristics

• Real-time: Data arrive sequentially and forecasts are made in real-time. As new data arrives, the model immediately makes forecasts and provides results.
• Interactivity: Online predictions are typically used to interact with and respond to users. Examples include personalization of online advertising and real-time recommendation systems.
• Open-world setting: Online forecasting requires forecasting for unknown data and classes, and the model adapts to make predictions even when new data and classes emerge.
• Resource efficiency: Online forecasting is performed in real-time and requires efficient use of resources. Optimizing forecasting speed and memory usage is therefore important.

Online forecasting uses different methods than batch forecasting. In batch forecasting, forecasts are made on the entire data set in batches, whereas in online forecasting, data arrives sequentially, so forecasts are required for each piece of data individually.

Online forecasting is widely used in systems and applications that require real-time data processing and responsiveness, for example, in web search engine auto-completion, speech recognition, machine translation, stock price prediction, and traffic forecasting.

Realizing online forecasting requires a complex system that involves a variety of elements, such as receiving real-time data streams, loading and forecasting models, and delivering results, as well as balancing the accuracy and response time of the forecast.

On the algorithms used for online forecasting

Various algorithms are used for online forecasting. The following describes some representative algorithms among them.

• Linear Models: Linear models use a linear combination of features to make predictions. Linear regression and logistic regression are typical linear models. In online forecasting, parameters are updated sequentially using stochastic gradient descent (SGD) or online learning algorithms.
• Neural Network: Neural networks are models consisting of multiple layers of neurons that perform nonlinear function approximation. Recurrent neural networks (RNN) as described in “Overview of RNN and examples of algorithms and implementations”, a type of neural network, and its advanced forms, LSTM described in “Overview of LSTM and Examples of Algorithms and Implementations” and GRU, are often used for online forecasting. Parameters can be updated by online learning or mini-batch learning.
• k-NN (k-Nearest Neighbors): k-NN is a method of forecasting by comparing new data to neighboring points in an existing data set. In online forecasting, neighborhoods are calculated each time data arrives and the closest data label is used for forecasting.
• Decision Trees and Ensemble Learning: Decision trees model a tree structure that partitions data based on conditions. In ensemble learning as described in “Overview of Ensemble Learning and Examples of Algorithms and Implementations”, predictions are made by combining multiple decision trees, and in online forecasting, the model is updated each time new data arrives.
• Bayesian Model: A Bayesian model estimates a posterior distribution from a prior distribution and data to make predictions. In online forecasting, the posterior distribution is updated each time new data arrives and forecasts are made, and Bayesian filters and particle filters are sometimes used for online forecasting.

Various algorithms are applied to the actual forecasting task. Care must be taken in the selection of algorithms and tuning of parameters, as online forecasting may change the forecast results depending on the order and arrival timing of the data.

Libraries and platforms used for online forecasting

Various libraries and platforms are used for online forecasting. The following is a list of some of the most representative ones.

• scikit-learn（Python）：scikit-learn is a widely used machine learning library in Python that provides a variety of algorithms and tools for predictive modeling. These address a variety of predictive tasks, including regression, classification, and clustering.
• TensorFlow（Python）：TensorFlow is an open source machine learning framework developed by Google that is used to build neural networks and create predictive models. They support a wide range of predictive tasks, including image recognition, natural language processing, and time series prediction.
• Keras（Python）：Keras is a high-level neural network library and runs on TensorFlow; Keras is easy to use and suitable for rapid prototyping. They are widely used to build predictive models such as convolutional and recurrent neural networks.
• PyTorch（Python）：PyTorch is an open source machine learning framework developed by Facebook for building neural networks. Like TensorFlow, they are suitable for a wide range of predictive tasks, including image processing, natural language processing, and time series prediction.
• Microsoft Azure Machine Learning：Microsoft Azure Machine Learning is a cloud-based machine learning platform that makes it easy to develop, train, and deploy predictive models. Python can be used to create models, scale, manage model versions, and automate deployment.
• Google Cloud AI Platform：Google Cloud AI Platform is a cloud-based machine learning platform that allows users to build, train, and deploy predictive models using Google’s machine learning technologies, including TensorFlow and Scikit-learn. TensorFlow and Scikit-learn can be used to develop models and train them on large data sets.

Next, we discuss the application of online forecasting.

Application Examples of Online Forecasting

Online forecasting is particularly useful in situations where data arrives sequentially, and we discuss some of their applications below.

• Web search engines: Online prediction is used to provide real-time search results when users enter keywords. Search engines return predicted results based on the query entered and past search history.
• Speech Recognition: Online prediction is used by speech recognition systems to process real-time speech data and convert it into text. Each time speech data arrives as a stream, the model makes predictions in real time.
• News feeds: Social media and news apps use online prediction to provide users with personalized news and content in real time. These can be used to predict the best content based on user behavior data and preferences.
• Traffic forecasting: Traffic management and navigation systems can predict traffic conditions based on real-time traffic data and user location information. Online forecasting can predict traffic congestion and optimal routes based on sequential data flows.
• Inventory management: Retail and manufacturing companies can use online forecasting to forecast demand and manage inventory. Based on sequential input of sales data and inventory information, demand can be forecasted and inventory levels optimized in real time.

These are just a few examples of online forecasting applications, but the actual applications are extensive, and online forecasting is effectively used in situations where data arrives sequentially. Online forecasting is especially important in situations where real-time response and rapid decision making are required.

Finally, we will discuss python implementations of these applications.

On an example implementation in python using online prediction in a web search engine

Several steps are required to implement online prediction in a web search engine in Python. Below is a general procedure and an example of its implementation.See “Overview of Search Systems and Examples of Implementations Focusing on Elasticsearch.”

1. Data collection and preprocessing: Collect the necessary data from the web search engine and perform any necessary preprocessing. For example, create a list of keywords or queries and retrieve search results related to them.

import requests

keywords = ['keyword1', 'keyword2', 'keyword3']
search_results = []

for keyword in keywords:
    url = f'https://example.com/search?q={keyword}'  # Incorporate keywords into search engine URLs
    response = requests.get(url)
    search_results.append(response.text)  # Add search results to the list

# Perform preprocessing such as formatting search_results into the appropriate format

2. Feature Extraction: Extract features to be used for prediction from the retrieved search results. For text data, features are usually calculated based on the frequency of keywords or TF-IDF. See also “Various Feature Engineering Methods and Python Implementation” for details.

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer

# Example of feature extraction for text data
vectorizer = CountVectorizer()
X_counts = vectorizer.fit_transform(search_results)

transformer = TfidfTransformer()
X_tfidf = transformer.fit_transform(X_counts)

3. Model training and prediction: train a prediction model using features and make predictions on new data. Specific models and algorithms are selected based on the forecasting task.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X_tfidf, labels, test_size=0.2)

# Model Training
model = LogisticRegression()
model.fit(X_train, y_train)

# Predictions for test data
y_pred = model.predict(X_test)

The above code shows some of the general procedures. Actual forecasting tasks may differ in terms of data preprocessing, feature extraction methods, model selection, etc., and these procedures should be appropriately customized according to specific requirements.

On an example implementation in python using online prediction in speech recognition

To implement online prediction in speech recognition in Python, it is common to combine the following steps. An example implementation is shown below. For more information on speech recognition systems, see also “Overview of Speech Recognition Systems and How to Create One.

1. Collect audio data: Collect audio data from microphones and recording files. Speech data can be input using an audio library (e.g. PyAudio). See also “How to Create a Speech Recognition System” for more information.

import pyaudio
import wave

def record_audio(filename, duration):
    CHUNK = 1024
    FORMAT = pyaudio.paInt16
    CHANNELS = 1
    RATE = 16000

    p = pyaudio.PyAudio()

    stream = p.open(format=FORMAT,
                    channels=CHANNELS,
                    rate=RATE,
                    input=True,
                    frames_per_buffer=CHUNK)

    frames = []

    print("Recording...")

    for i in range(0, int(RATE / CHUNK * duration)):
        data = stream.read(CHUNK)
        frames.append(data)

    print("Finished recording.")

    stream.stop_stream()
    stream.close()
    p.terminate()

    wf = wave.open(filename, 'wb')
    wf.setnchannels(CHANNELS)
    wf.setsampwidth(p.get_sample_size(FORMAT))
    wf.setframerate(RATE)
    wf.writeframes(b''.join(frames))
    wf.close()

# Record 5 seconds of audio and save it to a file
record_audio('audio.wav', 5)

2. Pre-processing of voice data: Pre-processing is performed on the collected voice data. Common preprocessing methods include audio filtering, noise reduction, and audio segmentation. See also “Noise Removal, Data Cleansing, and Interpolation of Missing Values in Machine Learning” for more details.

import librosa

def preprocess_audio(filename):
    # Loading voice data
    audio, sr = librosa.load(filename, sr=16000)

    # Perform preprocessing (e.g., spectral processing, noise reduction, etc.)
    processed_audio = ...

    return processed_audio

# Perform preprocessing of voice data
processed_audio = preprocess_audio('audio.wav')

3. Speech recognition model preparation and prediction: train speech recognition models and make predictions on preprocessed speech data. Common methods include deep learning-based speech recognition models (e.g., recurrent neural networks, transformers). For more information, see “Overview of python Keras and examples of its application to basic deep learning tasks” etc.

import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

# Model and preprocessor preparation
processor = Wav2Vec2Processor.from_pretrained('facebook/wav2vec2-base-960h')
model = Wav2Vec2ForCTC.from_pretrained('facebook/wav2vec2-base-960h')

# Converts preprocessed audio data to tensor
input_values = processor(processed_audio, return_tensors="pt").input_values

# Predicting Speech Recognition
with torch.no_grad():
    logits = model(input_values).logits

# Convert predicted results to text
predicted_ids = torch.argmax(logits, dim=-1)
transcriptions = processor.decode(predicted_ids[0])

print("Transcription:", transcriptions)

For an example implementation in python using online forecasting in news feeds

To implement online forecasting in news feeds in Python, it is common to combine the following steps. An example implementation is shown below.

1. Gathering News Data: Gather the information you need from news feeds. Using public APIs or news RSS feeds, it is possible to retrieve the titles and text of news articles. For details, see “Overview of web crawling technology and Python/Clojure implementation” etc.

import requests

def fetch_news_data():
    url = 'https://api.example.com/news'  # News API Endpoints
    response = requests.get(url)
    news_data = response.json()

    # Extract the information you need
    news_titles = [item['title'] for item in news_data]
    news_bodies = [item['body'] for item in news_data]

    return news_titles, news_bodies

# Get News Data
titles, bodies = fetch_news_data()

2. Text data preprocessing: Perform preprocessing on the collected news data. Common preprocessing methods include text cleaning, tokenization, stopword removal, and vectorization. For details, please refer to “Overview of Natural Language Processing and Examples of Various Implementations.

import nltk
from sklearn.feature_extraction.text import TfidfVectorizer

nltk.download('punkt')  # Run only when necessary

def preprocess_text(text):
    # Tokenization of text
    tokens = nltk.word_tokenize(text.lower())

    # Pre-processing, such as removal of stopwords

    # Turn preprocessed text back into a string
    preprocessed_text = ' '.join(tokens)

    return preprocessed_text

# Perform preprocessing of news title and body text
preprocessed_titles = [preprocess_text(title) for title in titles]
preprocessed_bodies = [preprocess_text(body) for body in bodies]

# Creating and Applying Vectorizers
vectorizer = TfidfVectorizer()
X_titles = vectorizer.fit_transform(preprocessed_titles)
X_bodies = vectorizer.transform(preprocessed_bodies)

3. Model training and forecasting: train a forecasting model based on news data and make forecasts for new news. Specific models and algorithms are selected based on the forecasting task.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Prepare target labels (as a tentative example, assume two classifications, positive and negative)
labels = [0, 1, 0, 1, 0, 1, ...]

# Combine features of title and body text
X_combined = X_titles + X_bodies

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X_combined, labels, test_size=0.2)

# Model Training
model = LogisticRegression()
model.fit(X_train, y_train)

# Predictions for test data
y_pred = model.predict(X_test)

For an example implementation in python using online forecasting in traffic forecasting

This section describes an example Python implementation of online forecasting for traffic forecasting. The following is a general procedure.

1. Collect traffic data: Collect traffic data. This can be obtained from traffic sensors, GPS data, or historical traffic information. If real-time traffic data is needed, APIs or data feeds can be used. For more information, see “Machine Learning and System Architecture for Data Streams (Time-Series Data)” and “Sensor Data & IOT Technologies“.

import requests
import json

def fetch_traffic_data():
    url = 'https://api.example.com/traffic'  # API endpoints for traffic data
    response = requests.get(url)
    traffic_data = response.json()

    # Extract the information you need
    timestamps = [item['timestamp'] for item in traffic_data]
    traffic_values = [item['value'] for item in traffic_data]

    return timestamps, traffic_values

# Obtain traffic data
timestamps, traffic_values = fetch_traffic_data()

2. Data Preprocessing: Preprocessing is performed on the collected traffic data. Common preprocessing methods include processing missing values, denoising, and scaling. See also “Noise Removal, Data Cleansing, and Interpolation of Missing Values in Machine Learning” for more details.

import numpy as np
from sklearn.preprocessing import MinMaxScaler

def preprocess_traffic_data(traffic_values):
    # Process missing values and perform denoising
    processed_traffic_values = ...

    # Perform scaling
    scaler = MinMaxScaler()
    scaled_traffic_values = scaler.fit_transform(np.array(processed_traffic_values).reshape(-1, 1))

    return scaled_traffic_values

# Perform pre-processing of traffic data
processed_traffic_values = preprocess_traffic_data(traffic_values)

3. Model Training and Forecasting: Using preprocessed traffic data, predictive models are trained to forecast future traffic. Common methods include time series forecasting models (e.g. ARIMA, LSTM). For more information, see “Examples of implementations for general time series analysis in R and Python” and “Time Series Analysis with Prophet“.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Prepare features and targets (e.g., traffic for the last 5 days as features)
window_size = 5
X = []
y = []

for i in range(len(processed_traffic_values) - window_size):
    X.append(processed_traffic_values[i:i+window_size])
    y.append(processed_traffic_values[i+window_size])

X = np.array(X)
y = np.array(y)

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Model Training
model = LinearRegression()
model.fit(X_train, y_train)

# Predictions for test data
y_pred = model.predict(X_test)

For an example implementation in python using online forecasting in inventory control

This section describes an example Python implementation of online forecasting for inventory control. The following is a general procedure.

1. Collect Inventory Data: Collect inventory data. This can be obtained from historical sales data, inventory reports, POS data, etc.

import pandas as pd

def fetch_inventory_data():
    # Load CSV file of inventory data (e.g., 'inventory.csv')
    inventory_data = pd.read_csv('inventory.csv')

    # Extract the information you need
    dates = pd.to_datetime(inventory_data['date'])
    stock_levels = inventory_data['stock_level']

    return dates, stock_levels

# Obtain inventory data
dates, stock_levels = fetch_inventory_data()

2. Data Preprocessing: Preprocessing is performed on the collected inventory data. Common preprocessing methods include processing missing values, removing outliers, and data completion. See also “Noise Removal, Data Cleansing, and Missing Value Interpolation in Machine Learning” for more details.

import numpy as np

def preprocess_inventory_data(stock_levels):
    # Perform missing value processing and outlier removal
    processed_stock_levels = ...

    # Perform data completion and interpolation
    interpolated_stock_levels = np.interp(
        np.arange(len(processed_stock_levels)),
        np.where(~np.isnan(processed_stock_levels))[0],
        processed_stock_levels[~np.isnan(processed_stock_levels)]
    )

    return interpolated_stock_levels

# Perform inventory data preprocessing
processed_stock_levels = preprocess_inventory_data(stock_levels)

3. Model Training and Forecasting: Using preprocessed inventory data, forecasting models are trained to predict future inventory. Common methods include time series forecasting models (e.g. ARIMA, SARIMA, Prophet). For more information, see “Example implementations for general time series analysis using R or Python” and “About time series analysis using Prophet“.

from statsmodels.tsa.arima.model import ARIMA

# Model Training
model = ARIMA(processed_stock_levels, order=(1, 1, 1))
model_fit = model.fit()

# Forecast inventory by specifying future time periods
future_periods = 7  # 7-day inventory forecast
forecast = model_fit.forecast(steps=future_periods)

# Get forecast results
predicted_stock_levels = forecast[0]

# Displays forecast results
print("Predicted stock levels:", predicted_stock_levels)

Reference Information and Reference Books

More information on online forecasting can be found in “Online Learning and Online Forecasting.

For reference, see “Online Forecasting”

“Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks: Online Environmental Field Reconstruction in Space and Time“

“Applicability of Online Sentiment Analysis for Stock Market Prediction: An Econometric Analysis“