Advanced Time-Series: Types, Methods, Applications and Top 20 Python Libraries đ
Advanced time series forecasting involves using machine learning, and deep learning techniques to predict future values of time-dependent data, accounting for complex patterns and seasonality, trends.
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đ Time series Types:
ⶠUnivariate
ⶠMultivariate
ⶠStationary
ⶠNon-Stationary
ⶠSeasonal
ⶠNon-Seasonal
ⶠIrregular
ⶠRegular
ⶠAdditive
ⶠMultiplicative
ⶠPeriodic
ⶠNon-Periodic
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âïž Here are several advanced time series forecasting methods:
âș LSTM (Long Short-Term Memory) Networks: A type of recurrent neural network (RNN) capable of learning long-term dependencies.
âș GRU (Gated Recurrent Unit) Networks: Similar to LSTM but with a simpler architecture.
âș Transformer Models: Uses attention mechanisms to capture dependencies without relying on sequential data.
âș TBATS (Trigonometric, Box-Cox, ARMA, Trend, Seasonal): Handles multiple seasonalities and complex seasonal patterns.
âș XGBoost (Extreme Gradient Boosting): An implementation of gradient-boosted decision trees designed for speed and performance.
âș N-BEATS (Neural Basis Expansion Analysis): A neural network-based approach designed specifically for time series forecasting.
âș TFT (Temporal Fusion Transformers): Combines the interpretability of transformers with temporal fusion for time series forecasting.
âș Large Language Models (LLMs): LLMs like GPT-4 can be adapted for time series forecasting by encoding time series as text, using embeddings, fine-tuning pre-trained models, combining with traditional methods, and leveraging contextual understanding from text-based data.
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đ Applications:
â Predictive Maintenance
â Healthcare Monitoring and Forecasting
â Energy Consumption Forecasting
â Supply Chain Optimization
â Natural Language Processing for Temporal Data
â Sensor Data Analysis
â Traffic Flow Prediction
â Sales and Revenue Forecasting
â Economic Indicators Forecasting
â Climate Modeling
â Stock Price Prediction
â Cryptocurrency Price Prediction
â Customer Churn Prediction
â Social Media Trend Analysis
â Fraud Detection
â Real-time Event Detection and Response
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I found the following 20 Libraries on Time-Series based on GitHub stars:
đSktime
đDarts
đtsfresh
đNeuralProphet
đSTUMPY
đpmdarima
đtslearn
đGluonTS
đPytorch-forecasting
đStatsForecast
đStreamz
đUber/orbit
đpyts
đNeuralForecast
đgreykite
đTSFEL
đseglearn
đtick
đAuto_TS
đDeepAR
Do you know other Time-series libraries or functions?
đSource:
https://github.com/PacktPublishing/Modern-Time-Series-Forecasting-with-Python
https://www.datasciencewithmarco.com/blog/timesnet-the-latest-advance-in-time-series-forecasting
https://machinelearningmastery.com/time-series-prediction-with-deep-learning-in-python-with-keras/