The goal of this thesis is to implement an anomaly detection tool using LSTM autoencoder and apply a novel method for uncertainty estimation using Bayesian Neural Networks (BNNs) based on a paper from Uber research group. Having a reliable anomaly detection tool and accurate uncertainty estimation is critical in many fields. At Telia, such a tool can be used in many different data domains like device logs to detect abnormal behaviours. Our method uses an autoencoder to extract important features and learn the encoded representation of the time series. This approach helps to capture testing data points coming from a different population. We then train a prediction model based on this encoder’s representation of data. An uncertainty estimation algorithm is used to estimate the model’s uncertainty, which breaks it down to three different sources: model uncertainty, model misspecification, and inherent noise. To get the first two, a Monte Carlo dropout approach is used which is simple to implement and easy to scale. For the third part, a bootstrap approach that estimates the noise level via the residual sum of squares on validation data is used. As a result, we could see that our proposed model can make a better prediction in comparison to our benchmarks. Although the difference is not big, yet it shows that making prediction based on encoding representation is more accurate. The anomaly detection results based on these predictions also show that our proposed model has a better performance than the benchmarks. This means that using autoencoder can improve both prediction and anomaly detection tasks. Additionally, we conclude that using deep neutral networks would show bigger improvement if the data has more complexity.