Quelles méthodes pouvez-vous utiliser pour valider l’exactitude des données de séries chronologiques ?

Outliers in Time series cannot be treated the same way as we do in usual data. We cannot directly use the Z score or IQR to remove outliers. What if we are looking at sales data in retail and revenue spikes every Thanksgiving week, year over year? That shouldn't be treated as an outlier. One of my favorite TS outlier handling packages is the tsoutliers package in R. The seasonality (If applicable) and trend (using smoothing) of the time series is accounted for before outliers are tagged. If a Time Series has known exogenous variables (holidays, price, promotions, etc.) ensure that it is also accounted for before deciding if a data point is an outlier or not.

Identifying and addressing outliers in time series data is crucial for accuracy. Outliers can skew statistical measures and adversely affect model performance. Robust statistical methods or visualization tools like box plots can help detect and manage outliers effectively, ensuring a more accurate representation of the underlying patterns in the data.

Outliers in time series data can derail results and model performance considerably, so detecting and handling them is crucial for accuracy. Visual inspection of plots is a great way to identify anomalies. Statistical methods like z-scores, modified z-scores, and the Tukey method label helps in identifying exceeding thresholds outliers. Supervised models like SVM and random forest can be trained on labeled data to learn outlier detection. Domain expertise should be used to validate that identified outliers are truly anomalous vs valid extreme values. it's easy to get confused between the two. An approach combining visual, statistical, and ML techniques provides an efficient way to accurately detect outliers.

Outliers can significantly skew time series analysis and forecasting. -> Tools like Azure's Anomaly Detector service can automatically identify anomalies and outliers in time series data. Based on my experience, incorporating robust statistical methods, such as the Interquartile Range (IQR) or Z-score analysis, can help in systematically identifying and addressing outliers, ensuring the data reflects true patterns without distortion.

Outliers are data points that deviate significantly from the rest of the data. They can be caused by measurement errors, data entry errors, or rare events. Outliers can affect the accuracy and performance of time series models, so they should be identified and handled appropriately. Some of the methods to detect and handle outliers are: box plots, z-scores, interquartile range, winsorization, trimming, and imputation

The type of imputation depends on the context and the kind of data. In my exposure to the retail domain, I have found that we can use - forward filling for regular (without discounts) price data. - smoothening the data and interpolating (check out supsmu() in R) for demand data. This has worked for me over a wide variety of retail data. - I wouldn't use backward filling in TS data as it could lead to data leakage.

Dealing with missing values is essential for maintaining the integrity of time series data. Imputation techniques, such as forward or backward filling, mean imputation, or using advanced methods like interpolation, can be employed. By addressing missing values appropriately, one can avoid biased analyses and enhance the reliability of models trained on the data.

It is common that a dataset can contain missing values. They can reduce the accuracy of a time series model if not properly addressed. Simple methods like replacing missing values with means, interpolation, or carrying forward/backward previous values can be an effective approach. Machine learning methods like KNN can suggest values based on similar instances. For validation, predicted datasets can be compared to the original data and metrics like imputation error and prediction accuracy calculated. Validating on artificially incomplete data tests model resilience to missing inputs for deployment. A combination of imputation, interpolation, and modelling provides accurate handling of missing time series data.

There are some packages like mice, missforest that can be handy to impute the missing values based on machine learning approaches. Ideall it can be used after all manual imputation/filling is done and if still missing values exidt, these packages would be at rescue. However, if you want a quick result to be seen and automated, we can give it a shot.

Missing values are data points that are not recorded or available for some reason. They can occur due to data collection issues, data processing issues, or data transmission issues. Missing values can also affect the accuracy and performance of time series models, so they should be handled appropriately. Some of the methods to handle missing values are: deletion, mean imputation, median imputation, mode imputation, linear interpolation, spline interpolation, and last observation carried forward

Stationarity in time series analysis refers to a statistical property where the characteristics of a time series data do not change over time. In a stationary time series we will have: Constant Mean: The mean of the series remains constant over time, Constant Variance: The variance or standard deviation of the series remains constant over time and Constant Autocorrelation Structure: The autocorrelation between the observations at any two points in time is the same for any given time lag. Not all models require strict stationarity, some handle trends or seasonality. We have ARIMA models that can handle stationary processes whereas SARIMA for seasonality.

Stationarity is a fundamental assumption in time series analysis. The Augmented Dickey-Fuller test and visual inspection of time series plots can help assess stationarity. Transformations like differencing may be applied to achieve stationarity. Ensuring this property is crucial for accurate forecasting and reliable model performance.

Time series is one interesting aspect in data science . How ever data accuracy is obviously needed in time series . In my experience mean variance auto correlation are important techniques . I have started my first job in time series related data only .

Stationarity is an important assumption for many time series models, so validating it is key for accuracy. Visual inspection of plots can check if the series varies around a constant mean and variance over time. Analytical tests like Dickey-Fuller and Kwiatkowski–Phillips–Schmidt–Shin (KPSS), formally test stationarity through hypothesis testing. Artificially sampling a stationary process and testing for stationarity can validate detection methods. If non-stationarity is detected, differencing, power transforms, detrending, or non-stationary models may be warranted. Validating stationarity assumptions by multiple methods improves time series modeling and forecasting accuracy.

Stationarity is a property of time series data that means that the statistical properties of the data, such as mean, variance, and autocorrelation, do not change over time. Stationarity is an important assumption for many time series models, such as ARIMA, VAR, and GARCH. Therefore, it is important to test whether the data is stationary or not before applying these models. Some of the methods to test for stationarity are: visual inspection, summary statistics, autocorrelation function, partial autocorrelation function, augmented Dickey-Fuller test, Phillips-Perron test, and Kwiatkowski-Phillips-Schmidt-Shin test.

For time series, windows based splitting works well. We use a set of past windows to train the data and predict on future windows. Even when doing cross validation we should use windows based cross validation. A scenario where recent windows in the training and older windows in the test should not occur.

Dividing time series data into training and testing sets is vital for model evaluation. The training set helps the model learn patterns, while the testing set assesses its generalization to new data. The temporal order of data is preserved during splitting, reflecting real-world scenarios and ensuring that the model performs well on unseen future data.

Maintaining chronological order is crucial for accuracy when doing train test splits. Rolling origin evaluation - This is an effective validation method that trains on an early time window then tests on the next window, incrementally moving the train/test split forward through the data. This realistically emulates streaming deployment. Expanding window validation - It starts with a small initial train window and gradually expands it forward in time, testing on the newest slice of data each iteration. Time-based k-fold cross-validation - This method splits the data into k partitions maintaining time order. Each fold acts as the test set while the others are used for training.

Splitting the data is a method of dividing the data into two or more subsets, such as training set, validation set, and test set. The purpose of splitting the data is to use different subsets for different stages of model development, such as model selection, model fitting, model evaluation, and model comparison. Splitting the data can help to avoid overfitting, underfitting, and bias-variance trade-off. Some of the methods to split the data are: random split, chronological split, rolling window, expanding window, and nested cross-validation

Importance of Splitting Data: Prevents overfitting: Training a model on all your data can lead to overfitting, where the model memorizes the training data and fails to perform well on new data. Splitting the data ensures the model learns general patterns, not just specific quirks of your dataset. Evaluates model performance: The test set allows you to assess how well your model generalizes to unseen data. This is critical for understanding the model's real-world applicability.

Quantifies Model's Accuracy: Error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), or Mean Absolute Percentage Error (MAPE) quantify the difference between predicted and actual values. Compares Models: These metrics allow you to compare the performance of different models and select the one that produces the most accurate forecasts for your specific data. Identifies Patterns: Visualizing errors with residual plots can reveal patterns and potential issues with the model, helping you refine your analysis.

I usually recommend sticking to at least a couple of different metrics that capture different aspects of accuracy (relative error, absolute error, positive/negative error) that are relevant to the problem at hand. It also makes sense to evaluate accuracy over multiple periods. If the Time series has yearly seasonality patterns, then it makes sense to evaluate accuracy at the crests and troughs. Similarly, if the Time series peaks during holidays (typical in retail and supply chain) we should evaluate error/accuracy in those periods too.

Accurately quantifying the model's predictive performance is essential. Metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE) provide insights into the model's accuracy. Choosing appropriate metrics depends on the specific goals and characteristics of the time series data being analyzed.

Choosing appropriate error metrics helps in validating time series models. Mean Absolute Scaled Error (MASE) is an effective error metric for time series that analyzes errors relative to a simple baseline forecast methods. Symmetric Mean Absolute Percentage Error (sMAPE) measures proportional errors while handling zero values. It gives a sense of relative fit. Rolling origin and expanding window validation provide multiple error measurements across different temporal holdout sets. Focusing on interpretable error metrics like MASE and sMAPE that accommodate challenges of time series data provides a robust validation approach. A multi-purpose validation strategy balances different sources of error for accurate assessment.

There's a high chance that time series models regurgitate most recent observations as predictions hence having a thorough evaluation framework is key. First off, a rolling average method can be used as a baseline model to understand how much additional lift the ML model provides. Metrics like MAE, MAPE can then be calculated and compared across baseline and ML models via: * Back-testing techniques * Out-of-time tests * Via a random validation set These can then be used to evaluate model!

Regular updates to time series data are necessary to capture evolving trends and patterns. This is particularly critical in dynamic environments where underlying factors influencing the data may change over time. Continuous monitoring and timely data refreshes ensure that models remain relevant and effective in making accurate predictions.

Updating the data is a method of incorporating new or revised data into the time series analysis or forecasting. The purpose of updating the data is to ensure that the data reflects the most recent and accurate information, and to account for any changes or trends in the data. Updating the data can help to improve the accuracy and performance of the time series models, and to adapt them to the changing conditions. Some of the methods to update the data are: batch update, online update, and hybrid update.

Time series models benefit significantly from regular updates with new data. This process helps capture the latest trends and patterns, ensuring the model remains relevant and accurate over time. Automating the data update and model retraining process, where feasible, can help maintain the accuracy of forecasts without manual intervention.

Feature engineering involves creating meaningful predictors from existing data, enhancing model performance. Careful selection of time series forecasting models, such as ARIMA or machine learning algorithms, plays a pivotal role. Hyperparameter tuning optimizes model settings, while cross-validation ensures robustness. Leveraging insights from the AI and LinkedIn community, along with collaboration with industry sponsors like Cisco, can provide valuable perspectives and resources for advancing time series data validation practices.

Beyond the specific methods mentioned, ensuring data quality, understanding the domain context, and continuously monitoring model performance against real-world outcomes are crucial. Additionally, considering external factors that could influence the time series (like seasonality, economic cycles, and unexpected events) and incorporating them into the analysis can significantly improve model accuracy and reliability

In addition to the provided methods, consider implementing cross-validation techniques specifically tailored for time series data. Time series cross-validation involves splitting the data into multiple folds while preserving the temporal order. This ensures that the model is validated on unseen future data, mimicking real-world deployment scenarios. Additionally, consider using domain-specific metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), or Forecast Bias to evaluate the accuracy of time series forecasts.