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Anomaly detection on time series

We can use Local Outlier Factor (LOF) or Random Forest along with a month column. Train these algorithms with your timeseries data (the actual values column) plus on month column to handle the ...
Anirban's user avatar
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Regression prediction for HVAC unit Best way to utilize available data?

use the cooling coils air handler fan power as the target, that is what you what to minimize/optimize and for the features use supply air temp, supply air setpoint, fan speed (if there is any), valve ...
ronjay s's user avatar
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Time Series forecasting feature creation/engineering

The answer is, it depends on your data. Converting a univariate forecasting problem to a univariate with exogenous variables can help improve forecasts, particularly for GBDTs. For models that take ...
Comte's user avatar
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Does nowcasting use cross sectional data?

Nowcasting is simple modelling the recent past, present and very near future, which is what you will read on almost every article and blog about the subject. One does not need to use cross-sectional ...
Comte's user avatar
  • 101
2 votes

How to check if an event affects time series

You could look into using event study methodologies, which are often used within economics and finance (for example to look at the impact of policy changes or stock market announcements). You can find ...
Oxbowerce's user avatar
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Time series test data dilema

You should not use shuffle for time series data as it will introduce lookahead bias. You can refer to below video as an example of how to do train test split for time series data. Tail should be test ...
Yogesh's user avatar
  • 101
2 votes
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Uneven spaced time series data, any advise on how to approach?

It depends what you want to do with it later. Certainly the easiest thing would be to do interpolation of some sort and resample the time series at evenly spaced points. More complex would be to use ...
Cryo's user avatar
  • 533
1 vote

Time Series Analysis and Price Elasticity

Can I ask what the logic is for dividing the log-differenced number of sales by the log-differenced moving average of price? Not saying the logic is wrong, I just haven't really seen this before. ...
aranglol's user avatar
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How bootstrapping works for prediction intervals?

1. Why are the PI outputs (gray lines) so different for certain setups like the below? As it is mentioned in the addressed referenced book by R. J Hyndman & G. Athanasopoulos in skforecast ...
Mario's user avatar
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Pearson correlation with overlapping data

You are complaining that your timeseries is only observed at discretized points, perhaps weekly. Yet the way you've formulated the problem implicitly makes the very strong assumption that prices are ...
J_H's user avatar
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1 vote
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Univariate time series forecasting with bimodal distribution

Consider simplifying seasonality by reducing to just 7-day totals. You have encountered a very common modeling situation where there are multiple Generating Processes out in the real world (such as ...
J_H's user avatar
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