Do Transformers Lose to Linear Models?
Long-Term Forecasting using Transformers may not be the way to go- 22250Murphy ≡ DeepGuide
The Critical Role of Loss Function Selection in Creating Accurate Time Series Forecasts
How your choice of loss function can make or break your time series forecasts- 22867Murphy ≡ DeepGuide
Time Series Forecasting with Facebook's Prophet in 10 Minutes – Part 1
Build a working model with 6 lines of code- 28193Murphy ≡ DeepGuide
XAI for Forecasting: Basis Expansion
NBEATS and other Interpretable Deep Forecasting Models- 23840Murphy ≡ DeepGuide
Forecasting with Granger Causality: Checking for Time Series Spurious Correlations
Hacking Granger Causality Test with ML Approaches- 27712Murphy ≡ DeepGuide
Dynamic Conformal Intervals for any Time Series Model
Apply and dynamically expand an interval using backtesting- 23074Murphy ≡ DeepGuide
Time Series Forecasting with Facebook's Prophet in 10 Minutes – Part 2
Model's performance and hyper-parameters fine tuning- 20304Murphy ≡ DeepGuide
Time Series for Climate Change: Forecasting Large Ocean Waves
How to use time series analysis and forecasting to tackle climate change- 22559Murphy ≡ DeepGuide
Time Series for Climate Change: Forecasting Energy Demand
How to use time series analysis and forecasting to tackle climate change- 26983Murphy ≡ DeepGuide
Winning with Simple, not even Linear Time-Series Models
If your dataset is small, the subsequent ideas might be useful- 26071Murphy ≡ DeepGuide
The Return of the Fallen: Transformers for Forecasting
Introducing a new transformer model: PatchTST- 25493Murphy ≡ DeepGuide
Want to Improve your Short-term Forecasting? Try Demand Sensing
When traditional forecasting approaches plateau in accuracy, how can we drive further forecasting improvements?- 24775Murphy ≡ DeepGuide
3 Types of Seasonality and How to Detect Them
Understanding time series seasonality- 26731Murphy ≡ DeepGuide
Real-Time Crowdedness Predictions for Train Travelers
With Wessel Radstok Travelers on the Dutch Railways can use the app from the Dutch railway agency to plan their trip. While planning the trip, the app shows a prediction for the crowdedness of the train in question. This is shown as three categories: low- 29479Murphy ≡ DeepGuide
Using Bayesian Networks to forecast ancillary service volume in hospitals
A Python example using diagnostic input variables- 28909Murphy ≡ DeepGuide
Five Practical Applications of the LSTM Model for Time Series, with Code
How to implement an advanced neural network model in several different time series contexts- 29690Murphy ≡ DeepGuide
The Comprehensive Guide to Moving Averages in Time Series Analysis
Exploring the Nuances of Simple Moving Averages and Exponentially Weighted Moving Averages- 25461Murphy ≡ DeepGuide
Exposing the Power of the Kalman Filter
As a data scientist we are occasionally faced with situations where we need to model a trend to predict future values. Whilst there is a...- 26816Murphy ≡ DeepGuide
Putting Your Forecasting Model to the Test: A Guide to Backtesting
Learn how to properly evaluate the performance of time series models through backtesting- 21426Murphy ≡ DeepGuide
Understanding Time Series Structural Changes
How to detect time series change points using Python- 21137Murphy ≡ DeepGuide
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