time series forecasting


New Book: Time Series Forecasting With Python

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Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this mega Ebook written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.


How to Use Dropout with LSTM Networks for Time Series Forecasting - Machine Learning Mastery

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Dropout is a regularization method where input and recurrent connections to LSTM units are probabilistically excluded from activation and weight updates while training a network. We can see that on average this model configuration achieved a test RMSE of about 92 monthly shampoo sales with a standard deviation of 5. In this case, the diagnostic plot shows a steady decrease in train and test RMSE to about 400-500 epochs, after which time it appears some overfitting may be occurring. Running the updated diagnostic creates a plot of the train and test RMSE performance of the model with input dropout after each training epoch.


Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis

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For arbitrary chosen store (Store 285) we received RMSE 0.11 for ARIMA model, RMSE 0.107 for XGBoost model and RMSE 0.093 for linear blending of ARIMA and XGBoost models. We also studied the case of time series forecasting using XGBoost model with time series approach and xgboost model based on independent and identically distributed variables. For arbitrary chosen store (Store 95) we received RMSE 0.138 for XGBoost model with time series approach and RMSE 0.118 for XGBoost model with i.i.d approach. Let us consider such features of sales time series as sales (variable logSales), mean sales per day for store (variable meanLogSales) and promo action (variable Promo).


How to Convert a Time Series to a Supervised Learning Problem in Python - Machine Learning Mastery

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In this tutorial, you will discover how to transform univariate and multivariate time series forecasting problems into supervised learning problems for use with machine learning algorithms. A key function to help transform time series data into a supervised learning problem is the Pandas shift() function. We can use the shift() function in Pandas to automatically create new framings of time series problems given the desired length of input and output sequences. This allows you to design a variety of different time step sequence type forecasting problems from a given univariate or multivariate time series.


How to Tune LSTM Hyperparameters with Keras for Time Series Forecasting - Machine Learning Mastery

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The persistence forecast (naive forecast) on the test dataset achieves an error of 136.761 monthly shampoo sales. This is where line plots of model skill over time (training iterations called epochs) will be created and studied for insight into how a given configuration performs and how it may be adjusted to elicit better performance. Some examples of test error show a possible inflection point around 600 epochs and may show a rising trend. It is exemplified by continued improvements on the training dataset and improvements followed by an inflection point and worsting skill in the test dataset.


Time Series Forecasting with the Long Short-Term Memory Network in Python

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A line plot of the test dataset (blue) compared to the predicted values (orange) is also created showing the persistence model forecast in context. It takes a NumPy array of the raw time series data and a lag or number of shifted series to create and use as inputs. The trend can be removed from the observations, then added back to forecasts later to return the prediction to the original scale and calculate a comparable error score. Running the example first prints the first 5 rows of the loaded data, then the first 5 rows of the scaled data, then the first 5 rows with the scale transform inverted, matching the original data.


New Book: Time Series Forecasting With Python

@machinelearnbot

Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this mega Ebook written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.


Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis

#artificialintelligence

For arbitrary chosen store (Store 285) we received RMSE 0.11 for ARIMA model, RMSE 0.107 for XGBoost model and RMSE 0.093 for linear blending of ARIMA and XGBoost models. We also studied the case of time series forecasting using XGBoost model with time series approach and xgboost model based on independent and identically distributed variables. For arbitrary chosen store (Store 95) we received RMSE 0.138 for XGBoost model with time series approach and RMSE 0.118 for XGBoost model with i.i.d approach. Let us consider such features of sales time series as sales (variable logSales), mean sales per day for store (variable meanLogSales) and promo action (variable Promo).



A Gentle Introduction to the Random Walk for Times Series Forecasting with Python - Machine Learning Mastery

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A Gentle Introduction to the Random Walk for Times Series Forecasting with Python Photo by Sam valadi, some rights reserved. We can use the randrange() function to generate a list of 1,000 random integers between 0 and 10. Persistence, or the naive forecast, is the best prediction we can make for a random walk time series. Many time series are random walks, particularly those of security prices over time.